The post How to Grid Search Deep Learning Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>This is because deep learning methods often require large amounts of data and large models, together resulting in models that take hours, days, or weeks to train.

In those cases where the datasets are smaller, such as univariate time series, it may be possible to use a grid search to tune the hyperparameters of a deep learning model.

In this tutorial, you will discover how to develop a framework to grid search hyperparameters for deep learning models.

After completing this tutorial, you will know:

- How to develop a generic grid searching framework for tuning model hyperparameters.
- How to grid search hyperparameters for a Multilayer Perceptron model on the airline passengers univariate time series forecasting problem.
- How to adapt the framework to grid search hyperparameters for convolutional and long short-term memory neural networks.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new book, with 25 step-by-step tutorials and full source code.

Let’s get started.

**Update May/2019**: Fixed small double assignment issue in the code (thanks Jameson).

This tutorial is divided into five parts; they are:

- Time Series Problem
- Grid Search Framework
- Grid Search Multilayer Perceptron
- Grid Search Convolutional Neural Network
- Grid Search Long Short-Term Memory Network

The ‘*monthly airline passenger*‘ dataset summarizes the monthly total number of international passengers in thousands on for an airline from 1949 to 1960.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-airline-passengers.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

# load series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0)

Once loaded, we can summarize the shape of the dataset in order to determine the number of observations.

# summarize shape print(series.shape)

We can then create a line plot of the series to get an idea of the structure of the series.

# plot pyplot.plot(series) pyplot.show()

We can tie all of this together; the complete example is listed below.

# load and plot dataset from pandas import read_csv from matplotlib import pyplot # load series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0) # summarize shape print(series.shape) # plot pyplot.plot(series) pyplot.show()

Running the example first prints the shape of the dataset.

(144, 1)

The dataset is monthly and has 12 years, or 144 observations. In our testing, we will use the last year, or 12 observations, as the test set.

A line plot is created. The dataset has an obvious trend and seasonal component. The period of the seasonal component is 12 months.

In this tutorial, we will introduce the tools for grid searching, but we will not optimize the model hyperparameters for this problem. Instead, we will demonstrate how to grid search the deep learning model hyperparameters generally and find models with some skill compared to a naive model.

From prior experiments, a naive model can achieve a root mean squared error, or RMSE, of 50.70 (remember the units are thousands of passengers) by persisting the value from 12 months ago (relative index -12).

The performance of this naive model provides a bound on a model that is considered skillful for this problem. Any model that achieves a predictive performance of lower than 50.70 on the last 12 months has skill.

It should be noted that a tuned ETS model can achieve an RMSE of 17.09 and a tuned SARIMA can achieve an RMSE of 13.89. These provide a lower bound on the expectations of a well-tuned deep learning model for this problem.

Now that we have defined our problem and expectations of model skill, we can look at defining the grid search test harness.

In this section, we will develop a grid search test harness that can be used to evaluate a range of hyperparameters for different neural network models, such as MLPs, CNNs, and LSTMs.

This section is divided into the following parts:

- Train-Test Split
- Series as Supervised Learning
- Walk-Forward Validation
- Repeat Evaluation
- Summarize Performance
- Worked Example

The first step is to split the loaded series into train and test sets.

We will use the first 11 years (132 observations) for training and the last 12 for the test set.

The *train_test_split()* function below will split the series taking the raw observations and the number of observations to use in the test set as arguments.

# split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:]

Next, we need to be able to frame the univariate series of observations as a supervised learning problem so that we can train neural network models.

A supervised learning framing of a series means that the data needs to be split into multiple examples that the model learns from and generalizes across.

Each sample must have both an input component and an output component.

The input component will be some number of prior observations, such as three years, or 36 time steps.

The output component will be the total sales in the next month because we are interested in developing a model to make one-step forecasts.

We can implement this using the shift() function on the pandas DataFrame. It allows us to shift a column down (forward in time) or back (backward in time). We can take the series as a column of data, then create multiple copies of the column, shifted forward or backward in time in order to create the samples with the input and output elements we require.

When a series is shifted down, NaN values are introduced because we don’t have values beyond the start of the series.

For example, the series defined as a column:

(t) 1 2 3 4

This column can be shifted and inserted as a column beforehand:

(t-1), (t) Nan, 1 1, 2 2, 3 3, 4 4 NaN

We can see that on the second row, the value 1 is provided as input as an observation at the prior time step, and 2 is the next value in the series that can be predicted, or learned by the model to be predicted when 1 is presented as input.

Rows with NaN values can be removed.

The *series_to_supervised()* function below implements this behavior, allowing you to specify the number of lag observations to use in the input and the number to use in the output for each sample. It will also remove rows that have NaN values as they cannot be used to train or test a model.

# transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values

Time series forecasting models can be evaluated on a test set using walk-forward validation.

Walk-forward validation is an approach where the model makes a forecast for each observation in the test dataset one at a time. After each forecast is made for a time step in the test dataset, the true observation for the forecast is added to the test dataset and made available to the model.

Simpler models can be refit with the observation prior to making the subsequent prediction. More complex models, such as neural networks, are not refit given the much greater computational cost.

Nevertheless, the true observation for the time step can then be used as part of the input for making the prediction on the next time step.

First, the dataset is split into train and test sets. We will call the *train_test_split()* function to perform this split and pass in the pre-specified number of observations to use as the test data.

A model will be fit once on the training dataset for a given configuration.

We will define a generic *model_fit()* function to perform this operation that can be filled in for the given type of neural network that we may be interested in later. The function takes the training dataset and the model configuration and returns the fit model ready for making predictions.

# fit a model def model_fit(train, config): return None

Each time step of the test dataset is enumerated. A prediction is made using the fit model.

Again, we will define a generic function named *model_predict()* that takes the fit model, the history, and the model configuration and makes a single one-step prediction.

# forecast with a pre-fit model def model_predict(model, history, config): return 0.0

The prediction is added to a list of predictions and the true observation from the test set is added to a list of observations that was seeded with all observations from the training dataset. This list is built up during each step in the walk-forward validation, allowing the model to make a one-step prediction using the most recent history.

All of the predictions can then be compared to the true values in the test set and an error measure calculated.

We will calculate the root mean squared error, or RMSE, between predictions and the true values.

RMSE is calculated as the square root of the average of the squared differences between the forecasts and the actual values. The *measure_rmse()* implements this below using the mean_squared_error() scikit-learn function to first calculate the mean squared error, or MSE, before calculating the square root.

# root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted))

The complete *walk_forward_validation()* function that ties all of this together is listed below.

It takes the dataset, the number of observations to use as the test set, and the configuration for the model, and returns the RMSE for the model performance on the test set.

# walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error

Neural network models are stochastic.

This means that, given the same model configuration and the same training dataset, a different internal set of weights will result each time the model is trained that will, in turn, have a different performance.

This is a benefit, allowing the model to be adaptive and find high performing configurations to complex problems.

It is also a problem when evaluating the performance of a model and in choosing a final model to use to make predictions.

To address model evaluation, we will evaluate a model configuration multiple times via walk-forward validation and report the error as the average error across each evaluation.

This is not always possible for large neural networks and may only make sense for small networks that can be fit in minutes or hours.

The *repeat_evaluate()* function below implements this and allows the number of repeats to be specified as an optional parameter that defaults to 10 and returns the mean RMSE score from all repeats.

# score a model, return None on failure def repeat_evaluate(data, config, n_test, n_repeats=10): # convert config to a key key = str(config) # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] # summarize score result = mean(scores) print('> Model[%s] %.3f' % (key, result)) return (key, result)

We now have all the pieces of the framework.

All that is left is a function to drive the search. We can define a *grid_search()* function that takes the dataset, a list of configurations to search, and the number of observations to use as the test set and perform the search.

Once mean scores are calculated for each config, the list of configurations is sorted in ascending order so that the best scores are listed first.

The complete function is listed below.

# grid search configs def grid_search(data, cfg_list, n_test): # evaluate configs scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores

Now that we have defined the elements of the test harness, we can tie them all together and define a simple persistence model.

We do not need to fit a model so the *model_fit()* function will be implemented to simply return None.

# fit a model def model_fit(train, config): return None

We will use the config to define a list of index offsets in the prior observations relative to the time to be forecasted that will be used as the prediction. For example, 12 will use the observation 12 months ago (-12) relative to the time to be forecasted.

# define config cfg_list = [1, 6, 12, 24, 36]

The *model_predict()* function can be implemented to use this configuration to persist the value at the negative relative offset.

# forecast with a pre-fit model def model_predict(model, history, offset): history[-offset]

The complete example of using the framework with a simple persistence model is listed below.

# grid search persistence models for airline passengers from math import sqrt from numpy import mean from pandas import read_csv from sklearn.metrics import mean_squared_error # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # fit a model def model_fit(train, config): return None # forecast with a pre-fit model def model_predict(model, history, offset): return history[-offset] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # score a model, return None on failure def repeat_evaluate(data, config, n_test, n_repeats=10): # convert config to a key key = str(config) # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] # summarize score result = mean(scores) print('> Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test): # evaluate configs scores = [repeat_evaluate(data, cfg, n_test) for cfg in cfg_list] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # define dataset series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # model configs cfg_list = [1, 6, 12, 24, 36] # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 10 configs for cfg, error in scores[:10]: print(cfg, error)

Running the example prints the RMSE of the model evaluated using walk-forward validation on the final 12 months of data.

Each model configuration is evaluated 10 times, although, because the model has no stochastic element, the score is the same each time.

At the end of the run, the configurations and RMSE for the top three performing model configurations are reported.

We can see, as we might have expected, that persisting the value from one year ago (relative offset -12) resulted in the best performance for the persistence model.

... > 110.274 > 110.274 > 110.274 > Model[36] 110.274 done 12 50.708316214732804 1 53.1515129919491 24 97.10990337413241 36 110.27352356753639 6 126.73495965991387

Now that we have a robust test harness for grid searching model hyperparameters, we can use it to evaluate a suite of neural network models.

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There are many aspects of the MLP that we may wish to tune.

We will define a very simple model with one hidden layer and define five hyperparameters to tune. They are:

**n_input**: The number of prior inputs to use as input for the model (e.g. 12 months).**n_nodes**: The number of nodes to use in the hidden layer (e.g. 50).**n_epochs**: The number of training epochs (e.g. 1000).**n_batch**: The number of samples to include in each mini-batch (e.g. 32).**n_diff**: The difference order (e.g. 0 or 12).

Modern neural networks can handle raw data with little pre-processing, such as scaling and differencing. Nevertheless, when it comes to time series data, sometimes differencing the series can make a problem easier to model.

Recall that differencing is the transform of the data such that a value of a prior observation is subtracted from the current observation, removing trend or seasonality structure.

We will add support for differencing to the grid search test harness, just in case it adds value to your specific problem. It does add value for the internal airline passengers dataset.

The *difference()* function below will calculate the difference of a given order for the dataset.

# difference dataset def difference(data, order): return [data[i] - data[i - order] for i in range(order, len(data))]

Differencing will be optional, where an order of 0 suggests no differencing, whereas an order 1 or order 12 will require that the data be differenced prior to fitting the model and that the predictions of the model will need the differencing reversed prior to returning the forecast.

We can now define the elements required to fit the MLP model in the test harness.

First, we must unpack the list of hyperparameters.

# unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config

Next, we must prepare the data, including the differencing, transforming the data to a supervised format and separating out the input and output aspects of the data samples.

# prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1]

We can now define and fit the model with the provided configuration.

# define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)

The complete implementation of the *model_fit()* function is listed below.

# fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

The five chosen hyperparameters are by no means the only or best hyperparameters of the model to tune. You may modify the function to tune other parameters, such as the addition and size of more hidden layers and much more.

Once the model is fit, we can use it to make forecasts.

If the data was differenced, the difference must be inverted for the prediction of the model. This involves adding the value at the relative offset from the history back to the value predicted by the model.

# invert difference correction = 0.0 if n_diff > 0: correction = history[-n_diff] ... # correct forecast if it was differenced return correction + yhat[0]

It also means that the history must be differenced so that the input data used to make the prediction has the expected form.

# calculate difference history = difference(history, n_diff)

Once prepared, we can use the history data to create a single sample as input to the model for making a one-step prediction.

The shape of one sample must be [1, n_input] where *n_input* is the chosen number of lag observations to use.

# shape input for model x_input = array(history[-n_input:]).reshape((1, n_input))

Finally, a prediction can be made.

# make forecast yhat = model.predict(x_input, verbose=0)

The complete implementation of the *model_predict()* function is listed below.

Next, we must define the range of values to try for each hyperparameter.

We can define a *model_configs()* function that creates a list of the different combinations of parameters to try.

We will define a small subset of configurations to try as an example, including a differencing of 12 months, which we expect will be required. You are encouraged to experiment with standalone models, review learning curve diagnostic plots, and use information about the domain to set ranges of values of the hyperparameters to grid search.

You are also encouraged to repeat the grid search to narrow in on ranges of values that appear to show better performance.

An implementation of the *model_configs()* function is listed below.

# create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_nodes = [50, 100] n_epochs = [100] n_batch = [1, 150] n_diff = [0, 12] # create configs configs = list() for i in n_input: for j in n_nodes: for k in n_epochs: for l in n_batch: for m in n_diff: cfg = [i, j, k, l, m] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs

We now have all of the pieces needed to grid search MLP models for a univariate time series forecasting problem.

The complete example is listed below.

# grid search mlps for airline passengers from math import sqrt from numpy import array from numpy import mean from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, order): return [data[i] - data[i - order] for i in range(order, len(data))] # fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with the fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) # shape input for model x_input = array(history[-n_input:]).reshape((1, n_input)) # make forecast yhat = model.predict(x_input, verbose=0) # correct forecast if it was differenced return correction + yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # score a model, return None on failure def repeat_evaluate(data, config, n_test, n_repeats=10): # convert config to a key key = str(config) # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] # summarize score result = mean(scores) print('> Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test): # evaluate configs scores = [repeat_evaluate(data, cfg, n_test) for cfg in cfg_list] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_nodes = [50, 100] n_epochs = [100] n_batch = [1, 150] n_diff = [0, 12] # create configs configs = list() for i in n_input: for j in n_nodes: for k in n_epochs: for l in n_batch: for m in n_diff: cfg = [i, j, k, l, m] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs # define dataset series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # model configs cfg_list = model_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example, we can see that there are a total of eight configurations to be evaluated by the framework.

Each config will be evaluated 10 times; that means 10 models will be created and evaluated using walk-forward validation to calculate an RMSE score before an average of those 10 scores is reported and used to score the configuration.

The scores are then sorted and the top 3 configurations with the lowest RMSE are reported at the end. A skillful model configuration was found as compared to a naive model that reported an RMSE of 50.70.

We can see that the best RMSE of 18.98 was achieved with a configuration of [12, 100, 100, 1, 12], which we know can be interpreted as:

**n_input**: 12**n_nodes**: 100**n_epochs**: 100**n_batch**: 1**n_diff**: 12

A truncated example output of the grid search is listed below.

Your specific scores may vary given the stochastic nature of the algorithm.

Total configs: 8 > 20.707 > 29.111 > 17.499 > 18.918 > 28.817 ... > 21.015 > 20.208 > 18.503 > Model[[12, 100, 100, 150, 12]] 19.674 done [12, 100, 100, 1, 12] 18.982720013625606 [12, 50, 100, 150, 12] 19.33004059448595 [12, 100, 100, 1, 0] 19.5389405532858

We can now adapt the framework to grid search CNN models.

Much the same set of hyperparameters can be searched as with the MLP model, except the number of nodes in the hidden layer can be replaced by the number of filter maps and kernel size in the convolutional layers.

The chosen set of hyperparameters to grid search in the CNN model are as follows:

**n_input**: The number of prior inputs to use as input for the model (e.g. 12 months).**n_filters**: The number of filter maps in the convolutional layer (e.g. 32).**n_kernel**: The kernel size in the convolutional layer (e.g. 3).**n_epochs**: The number of training epochs (e.g. 1000).**n_batch**: The number of samples to include in each mini-batch (e.g. 32).**n_diff**: The difference order (e.g. 0 or 12).

Some additional hyperparameters that you may wish to investigate are the use of two convolutional layers before a pooling layer, the repetition of the convolutional and pooling layer pattern, the use of dropout, and more.

We will define a very simple CNN model with one convolutional layer and one max pooling layer.

# define model model = Sequential() model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam')

The data must be prepared in much the same way as for the MLP.

Unlike the MLP that expects the input data to have the shape [samples, features], the 1D CNN model expects the data to have the shape [*samples, timesteps, features*] where features maps onto channels and in this case 1 for the one variable we measure each month.

# reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features))

The complete implementation of the *model_fit()* function is listed below.

# fit a model def model_fit(train, config): # unpack config n_input, n_filters, n_kernel, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features)) # define model model = Sequential() model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Making a prediction with a fit CNN model is very much like making a prediction with a fit MLP.

Again, the only difference is that the one sample worth of input data must have a three-dimensional shape.

x_input = array(history[-n_input:]).reshape((1, n_input, 1))

The complete implementation of the *model_predict()* function is listed below.

# forecast with the fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0]

Finally, we can define a list of configurations for the model to evaluate. As before, we can do this by defining lists of hyperparameter values to try that are combined into a list. We will try a small number of configurations to ensure the example executes in a reasonable amount of time.

The complete *model_configs()* function is listed below.

# create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_filters = [64] n_kernels = [3, 5] n_epochs = [100] n_batch = [1, 150] n_diff = [0, 12] # create configs configs = list() for a in n_input: for b in n_filters: for c in n_kernels: for d in n_epochs: for e in n_batch: for f in n_diff: cfg = [a,b,c,d,e,f] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs

We now have all of the elements needed to grid search the hyperparameters of a convolutional neural network for univariate time series forecasting.

The complete example is listed below.

# grid search cnn for airline passengers from math import sqrt from numpy import array from numpy import mean from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, order): return [data[i] - data[i - order] for i in range(order, len(data))] # fit a model def model_fit(train, config): # unpack config n_input, n_filters, n_kernel, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features)) # define model model = Sequential() model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with the fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # score a model, return None on failure def repeat_evaluate(data, config, n_test, n_repeats=10): # convert config to a key key = str(config) # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] # summarize score result = mean(scores) print('> Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test): # evaluate configs scores = [repeat_evaluate(data, cfg, n_test) for cfg in cfg_list] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_filters = [64] n_kernels = [3, 5] n_epochs = [100] n_batch = [1, 150] n_diff = [0, 12] # create configs configs = list() for a in n_input: for b in n_filters: for c in n_kernels: for d in n_epochs: for e in n_batch: for f in n_diff: cfg = [a,b,c,d,e,f] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs # define dataset series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # model configs cfg_list = model_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 10 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example, we can see that only eight distinct configurations are evaluated.

We can see that a configuration of [12, 64, 5, 100, 1, 12] achieved an RMSE of 18.89, which is skillful as compared to a naive forecast model that achieved 50.70.

We can unpack this configuration as:

**n_input**: 12**n_filters**: 64**n_kernel**: 5**n_epochs**: 100**n_batch**: 1**n_diff**: 12

A truncated example output of the grid search is listed below.

Your specific scores may vary given the stochastic nature of the algorithm.

Total configs: 8 > 23.372 > 28.317 > 31.070 ... > 20.923 > 18.700 > 18.210 > Model[[12, 64, 5, 100, 150, 12]] 19.152 done [12, 64, 5, 100, 1, 12] 18.89593462072732 [12, 64, 5, 100, 150, 12] 19.152486150334234 [12, 64, 3, 100, 150, 12] 19.44680151564605

We can now adopt the framework for grid searching the hyperparameters of an LSTM model.

The hyperparameters for the LSTM model will be the same five as the MLP; they are:

**n_input**: The number of prior inputs to use as input for the model (e.g. 12 months).**n_nodes**: The number of nodes to use in the hidden layer (e.g. 50).**n_epochs**: The number of training epochs (e.g. 1000).**n_batch**: The number of samples to include in each mini-batch (e.g. 32).**n_diff**: The difference order (e.g. 0 or 12).

We will define a simple LSTM model with a single hidden LSTM layer and the number of nodes specifying the number of units in this layer.

# define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)

It may be interesting to explore tuning additional configurations such as the use of a bidirectional input layer, stacked LSTM layers, and even hybrid models with CNN or ConvLSTM input models.

As with the CNN model, the LSTM model expects input data to have a three-dimensional shape for the samples, time steps, and features.

# reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features))

The complete implementation of the *model_fit()* function is listed below.

# fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Also like the CNN, the single input sample used to make a prediction must also be reshaped into the expected three-dimensional structure.

# reshape sample into [samples, timesteps, features] x_input = array(history[-n_input:]).reshape((1, n_input, 1))

The complete *model_predict()* function is listed below.

# forecast with the fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) # reshape sample into [samples, timesteps, features] x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0]

We can now define the function used to create the list of model configurations to evaluate.

The LSTM model is quite a bit slower to train than MLP and CNN models; as such, you may want to evaluate fewer configurations per run.

We will define a very simple set of two configurations to explore: stochastic and batch gradient descent.

# create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_nodes = [100] n_epochs = [50] n_batch = [1, 150] n_diff = [12] # create configs configs = list() for i in n_input: for j in n_nodes: for k in n_epochs: for l in n_batch: for m in n_diff: cfg = [i, j, k, l, m] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs

We now have everything we need to grid search hyperparameters for the LSTM model for univariate time series forecasting.

The complete example is listed below.

# grid search lstm for airline passengers from math import sqrt from numpy import array from numpy import mean from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, order): return [data[i] - data[i - order] for i in range(order, len(data))] # fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) # transform series into supervised format data = series_to_supervised(train, n_in=n_input) # separate inputs and outputs train_x, train_y = data[:, :-1], data[:, -1] # reshape input data into [samples, timesteps, features] n_features = 1 train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with the fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) # reshape sample into [samples, timesteps, features] x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # score a model, return None on failure def repeat_evaluate(data, config, n_test, n_repeats=10): # convert config to a key key = str(config) # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] # summarize score result = mean(scores) print('> Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test): # evaluate configs scores = [repeat_evaluate(data, cfg, n_test) for cfg in cfg_list] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a list of configs to try def model_configs(): # define scope of configs n_input = [12] n_nodes = [100] n_epochs = [50] n_batch = [1, 150] n_diff = [12] # create configs configs = list() for i in n_input: for j in n_nodes: for k in n_epochs: for l in n_batch: for m in n_diff: cfg = [i, j, k, l, m] configs.append(cfg) print('Total configs: %d' % len(configs)) return configs # define dataset series = read_csv('monthly-airline-passengers.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # model configs cfg_list = model_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 10 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example, we can see that only two distinct configurations are evaluated.

We can see that a configuration of [12, 100, 50, 1, 12] achieved an RMSE of 21.24, which is skillful as compared to a naive forecast model that achieved 50.70.

The model requires a lot more tuning and may do much better with a hybrid configuration, such as having a CNN model as input.

We can unpack this configuration as:

**n_input**: 12**n_nodes**: 100**n_epochs**: 50**n_batch**: 1**n_diff**: 12

A truncated example output of the grid search is listed below.

Your specific scores may vary given the stochastic nature of the algorithm.

Total configs: 2 > 20.488 > 17.718 > 21.213 ... > 22.300 > 20.311 > 21.322 > Model[[12, 100, 50, 150, 12]] 21.260 done [12, 100, 50, 1, 12] 21.243775750634093 [12, 100, 50, 150, 12] 21.259553398553606

This section lists some ideas for extending the tutorial that you may wish to explore.

**More Configurations**. Explore a large suite of configurations for one of the models and see if you can find a configuration that results in better performance.**Data Scaling**. Update the grid search framework to also support the scaling (normalization and/or standardization) of data both before fitting the model and inverting the transform for predictions.**Network Architecture**. Explore the grid searching larger architectural changes for a given model, such as the addition of more hidden layers.**New Dataset**. Explore the grid search of a given model in a new univariate time series dataset.**Multivariate**. Update the grid search framework to support small multivariate time series datasets, e.g. datasets with multiple input variables.

If you explore any of these extensions, I’d love to know.

This section provides more resources on the topic if you are looking to go deeper.

- How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras
- Keras Core Layers API
- Keras Convolutional Layers API
- Keras Recurrent Layers API

In this tutorial, you discovered how to develop a framework to grid search hyperparameters for deep learning models.

Specifically, you learned:

- How to develop a generic grid searching framework for tuning model hyperparameters.
- How to grid search hyperparameters for a Multilayer Perceptron model on the airline passengers univariate time series forecasting problem.
- How to adapt the framework to grid search hyperparameters for convolutional and long short-term memory neural networks.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Grid Search Deep Learning Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Develop LSTM Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>There are many types of LSTM models that can be used for each specific type of time series forecasting problem.

In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.

The objective of this tutorial is to provide standalone examples of each model on each type of time series problem as a template that you can copy and adapt for your specific time series forecasting problem.

After completing this tutorial, you will know:

- How to develop LSTM models for univariate time series forecasting.
- How to develop LSTM models for multivariate time series forecasting.
- How to develop LSTM models for multi-step time series forecasting.

This is a large and important post; you may want to bookmark it for future reference.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new book, with 25 step-by-step tutorials and full source code.

Let’s get started.

In this tutorial, we will explore how to develop a suite of different types of LSTM models for time series forecasting.

The models are demonstrated on small contrived time series problems intended to give the flavor of the type of time series problem being addressed. The chosen configuration of the models is arbitrary and not optimized for each problem; that was not the goal.

This tutorial is divided into four parts; they are:

- Univariate LSTM Models
- Data Preparation
- Vanilla LSTM
- Stacked LSTM
- Bidirectional LSTM
- CNN LSTM
- ConvLSTM

- Multivariate LSTM Models
- Multiple Input Series.
- Multiple Parallel Series.

- Multi-Step LSTM Models
- Data Preparation
- Vector Output Model
- Encoder-Decoder Model

- Multivariate Multi-Step LSTM Models
- Multiple Input Multi-Step Output.
- Multiple Parallel Input and Multi-Step Output.

LSTMs can be used to model univariate time series forecasting problems.

These are problems comprised of a single series of observations and a model is required to learn from the series of past observations to predict the next value in the sequence.

We will demonstrate a number of variations of the LSTM model for univariate time series forecasting.

This section is divided into six parts; they are:

- Data Preparation
- Vanilla LSTM
- Stacked LSTM
- Bidirectional LSTM
- CNN LSTM
- ConvLSTM

Each of these models are demonstrated for one-step univariate time series forecasting, but can easily be adapted and used as the input part of a model for other types of time series forecasting problems.

Before a univariate series can be modeled, it must be prepared.

The LSTM model will learn a function that maps a sequence of past observations as input to an output observation. As such, the sequence of observations must be transformed into multiple examples from which the LSTM can learn.

Consider a given univariate sequence:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We can divide the sequence into multiple input/output patterns called samples, where three time steps are used as input and one time step is used as output for the one-step prediction that is being learned.

X, y 10, 20, 30 40 20, 30, 40 50 30, 40, 50 60 ...

The *split_sequence()* function below implements this behavior and will split a given univariate sequence into multiple samples where each sample has a specified number of time steps and the output is a single time step.

# split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on our small contrived dataset above.

The complete example is listed below.

# univariate data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into six samples where each sample has three input time steps and one output time step.

[10 20 30] 40 [20 30 40] 50 [30 40 50] 60 [40 50 60] 70 [50 60 70] 80 [60 70 80] 90

Now that we know how to prepare a univariate series for modeling, let’s look at developing LSTM models that can learn the mapping of inputs to outputs, starting with a Vanilla LSTM.

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

A Vanilla LSTM is an LSTM model that has a single hidden layer of LSTM units, and an output layer used to make a prediction.

We can define a Vanilla LSTM for univariate time series forecasting as follows.

# define model model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

Key in the definition is the shape of the input; that is what the model expects as input for each sample in terms of the number of time steps and the number of features.

We are working with a univariate series, so the number of features is one, for one variable.

The number of time steps as input is the number we chose when preparing our dataset as an argument to the *split_sequence()* function.

The shape of the input for each sample is specified in the *input_shape* argument on the definition of first hidden layer.

We almost always have multiple samples, therefore, the model will expect the input component of training data to have the dimensions or shape:

[samples, timesteps, features]

Our *split_sequence()* function in the previous section outputs the X with the shape [*samples, timesteps*], so we easily reshape it to have an additional dimension for the one feature.

# reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features))

In this case, we define a model with 50 LSTM units in the hidden layer and an output layer that predicts a single numerical value.

The model is fit using the efficient Adam version of stochastic gradient descent and optimized using the mean squared error, or ‘*mse*‘ loss function.

Once the model is defined, we can fit it on the training dataset.

# fit model model.fit(X, y, epochs=200, verbose=0)

After the model is fit, we can use it to make a prediction.

We can predict the next value in the sequence by providing the input:

[70, 80, 90]

And expecting the model to predict something like:

[100]

The model expects the input shape to be three-dimensional with [*samples, timesteps, features*], therefore, we must reshape the single input sample before making the prediction.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

We can tie all of this together and demonstrate how to develop a Vanilla LSTM for univariate time series forecasting and make a single prediction.

# univariate lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=200, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

Your results may vary given the stochastic nature of the algorithm; try running the example a few times.

We can see that the model predicts the next value in the sequence.

[[102.09213]]

Multiple hidden LSTM layers can be stacked one on top of another in what is referred to as a Stacked LSTM model.

An LSTM layer requires a three-dimensional input and LSTMs by default will produce a two-dimensional output as an interpretation from the end of the sequence.

We can address this by having the LSTM output a value for each time step in the input data by setting the *return_sequences=True* argument on the layer. This allows us to have 3D output from hidden LSTM layer as input to the next.

We can therefore define a Stacked LSTM as follows.

# define model model = Sequential() model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features))) model.add(LSTM(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

We can tie this together; the complete code example is listed below.

# univariate stacked lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a univariate sequence def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features))) model.add(LSTM(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=200, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example predicts the next value in the sequence, which we expect would be 100.

[[102.47341]]

On some sequence prediction problems, it can be beneficial to allow the LSTM model to learn the input sequence both forward and backwards and concatenate both interpretations.

This is called a Bidirectional LSTM.

We can implement a Bidirectional LSTM for univariate time series forecasting by wrapping the first hidden layer in a wrapper layer called Bidirectional.

An example of defining a Bidirectional LSTM to read input both forward and backward is as follows.

# define model model = Sequential() model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

The complete example of the Bidirectional LSTM for univariate time series forecasting is listed below.

# univariate bidirectional lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import Bidirectional # split a univariate sequence def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=200, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example predicts the next value in the sequence, which we expect would be 100.

[[101.48093]]

A convolutional neural network, or CNN for short, is a type of neural network developed for working with two-dimensional image data.

The CNN can be very effective at automatically extracting and learning features from one-dimensional sequence data such as univariate time series data.

A CNN model can be used in a hybrid model with an LSTM backend where the CNN is used to interpret subsequences of input that together are provided as a sequence to an LSTM model to interpret. This hybrid model is called a CNN-LSTM.

The first step is to split the input sequences into subsequences that can be processed by the CNN model. For example, we can first split our univariate time series data into input/output samples with four steps as input and one as output. Each sample can then be split into two sub-samples, each with two time steps. The CNN can interpret each subsequence of two time steps and provide a time series of interpretations of the subsequences to the LSTM model to process as input.

We can parameterize this and define the number of subsequences as *n_seq* and the number of time steps per subsequence as *n_steps*. The input data can then be reshaped to have the required structure:

[samples, subsequences, timesteps, features]

For example:

# choose a number of time steps n_steps = 4 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, subsequences, timesteps, features] n_features = 1 n_seq = 2 n_steps = 2 X = X.reshape((X.shape[0], n_seq, n_steps, n_features))

We want to reuse the same CNN model when reading in each sub-sequence of data separately.

This can be achieved by wrapping the entire CNN model in a TimeDistributed wrapper that will apply the entire model once per input, in this case, once per input subsequence.

The CNN model first has a convolutional layer for reading across the subsequence that requires a number of filters and a kernel size to be specified. The number of filters is the number of reads or interpretations of the input sequence. The kernel size is the number of time steps included of each ‘read’ operation of the input sequence.

The convolution layer is followed by a max pooling layer that distills the filter maps down to 1/2 of their size that includes the most salient features. These structures are then flattened down to a single one-dimensional vector to be used as a single input time step to the LSTM layer.

model.add(TimeDistributed(Conv1D(filters=64, kernel_size=1, activation='relu'), input_shape=(None, n_steps, n_features))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten()))

Next, we can define the LSTM part of the model that interprets the CNN model’s read of the input sequence and makes a prediction.

model.add(LSTM(50, activation='relu')) model.add(Dense(1))

We can tie all of this together; the complete example of a CNN-LSTM model for univariate time series forecasting is listed below.

# univariate cnn lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import Flatten from keras.layers import TimeDistributed from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 4 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, subsequences, timesteps, features] n_features = 1 n_seq = 2 n_steps = 2 X = X.reshape((X.shape[0], n_seq, n_steps, n_features)) # define model model = Sequential() model.add(TimeDistributed(Conv1D(filters=64, kernel_size=1, activation='relu'), input_shape=(None, n_steps, n_features))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten())) model.add(LSTM(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=500, verbose=0) # demonstrate prediction x_input = array([60, 70, 80, 90]) x_input = x_input.reshape((1, n_seq, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example predicts the next value in the sequence, which we expect would be 100.

[[101.69263]]

A type of LSTM related to the CNN-LSTM is the ConvLSTM, where the convolutional reading of input is built directly into each LSTM unit.

The ConvLSTM was developed for reading two-dimensional spatial-temporal data, but can be adapted for use with univariate time series forecasting.

The layer expects input as a sequence of two-dimensional images, therefore the shape of input data must be:

[samples, timesteps, rows, columns, features]

For our purposes, we can split each sample into subsequences where timesteps will become the number of subsequences, or *n_seq*, and columns will be the number of time steps for each subsequence, or *n_steps*. The number of rows is fixed at 1 as we are working with one-dimensional data.

We can now reshape the prepared samples into the required structure.

# choose a number of time steps n_steps = 4 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, rows, columns, features] n_features = 1 n_seq = 2 n_steps = 2 X = X.reshape((X.shape[0], n_seq, 1, n_steps, n_features))

We can define the ConvLSTM as a single layer in terms of the number of filters and a two-dimensional kernel size in terms of (rows, columns). As we are working with a one-dimensional series, the number of rows is always fixed to 1 in the kernel.

The output of the model must then be flattened before it can be interpreted and a prediction made.

model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation='relu', input_shape=(n_seq, 1, n_steps, n_features))) model.add(Flatten())

The complete example of a ConvLSTM for one-step univariate time series forecasting is listed below.

# univariate convlstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import Flatten from keras.layers import ConvLSTM2D # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 4 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, rows, columns, features] n_features = 1 n_seq = 2 n_steps = 2 X = X.reshape((X.shape[0], n_seq, 1, n_steps, n_features)) # define model model = Sequential() model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation='relu', input_shape=(n_seq, 1, n_steps, n_features))) model.add(Flatten()) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=500, verbose=0) # demonstrate prediction x_input = array([60, 70, 80, 90]) x_input = x_input.reshape((1, n_seq, 1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example predicts the next value in the sequence, which we expect would be 100.

[[103.68166]]

Now that we have looked at LSTM models for univariate data, let’s turn our attention to multivariate data.

Multivariate time series data means data where there is more than one observation for each time step.

There are two main models that we may require with multivariate time series data; they are:

- Multiple Input Series.
- Multiple Parallel Series.

Let’s take a look at each in turn.

A problem may have two or more parallel input time series and an output time series that is dependent on the input time series.

The input time series are parallel because each series has an observation at the same time steps.

We can demonstrate this with a simple example of two parallel input time series where the output series is the simple addition of the input series.

# define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])

We can reshape these three arrays of data as a single dataset where each row is a time step, and each column is a separate time series. This is a standard way of storing parallel time series in a CSV file.

# convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq))

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) print(dataset)

Running the example prints the dataset with one row per time step and one column for each of the two input and one output parallel time series.

[[ 10 15 25] [ 20 25 45] [ 30 35 65] [ 40 45 85] [ 50 55 105] [ 60 65 125] [ 70 75 145] [ 80 85 165] [ 90 95 185]]

As with the univariate time series, we must structure these data into samples with input and output elements.

An LSTM model needs sufficient context to learn a mapping from an input sequence to an output value. LSTMs can support parallel input time series as separate variables or features. Therefore, we need to split the data into samples maintaining the order of observations across the two input sequences.

If we chose three input time steps, then the first sample would look as follows:

Input:

10, 15 20, 25 30, 35

Output:

65

That is, the first three time steps of each parallel series are provided as input to the model and the model associates this with the value in the output series at the third time step, in this case, 65.

We can see that, in transforming the time series into input/output samples to train the model, that we will have to discard some values from the output time series where we do not have values in the input time series at prior time steps. In turn, the choice of the size of the number of input time steps will have an important effect on how much of the training data is used.

We can define a function named *split_sequences()* that will take a dataset as we have defined it with rows for time steps and columns for parallel series and return input/output samples.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can test this function on our dataset using three time steps for each input time series as input.

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the X and y components.

We can see that the X component has a three-dimensional structure.

The first dimension is the number of samples, in this case 7. The second dimension is the number of time steps per sample, in this case 3, the value specified to the function. Finally, the last dimension specifies the number of parallel time series or the number of variables, in this case 2 for the two parallel series.

This is the exact three-dimensional structure expected by an LSTM as input. The data is ready to use without further reshaping.

We can then see that the input and output for each sample is printed, showing the three time steps for each of the two input series and the associated output for each sample.

(7, 3, 2) (7,) [[10 15] [20 25] [30 35]] 65 [[20 25] [30 35] [40 45]] 85 [[30 35] [40 45] [50 55]] 105 [[40 45] [50 55] [60 65]] 125 [[50 55] [60 65] [70 75]] 145 [[60 65] [70 75] [80 85]] 165 [[70 75] [80 85] [90 95]] 185

We are now ready to fit an LSTM model on this data.

Any of the varieties of LSTMs in the previous section can be used, such as a Vanilla, Stacked, Bidirectional, CNN, or ConvLSTM model.

We will use a Vanilla LSTM where the number of time steps and parallel series (features) are specified for the input layer via the *input_shape* argument.

# define model model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

When making a prediction, the model expects three time steps for two input time series.

We can predict the next value in the output series providing the input values of:

80, 85 90, 95 100, 105

The shape of the one sample with three time steps and two variables must be [1, 3, 2].

We would expect the next value in the sequence to be 100 + 105, or 205.

# demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

The complete example is listed below.

# multivariate lstm example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=200, verbose=0) # demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[208.13531]]

An alternate time series problem is the case where there are multiple parallel time series and a value must be predicted for each.

For example, given the data from the previous section:

[[ 10 15 25] [ 20 25 45] [ 30 35 65] [ 40 45 85] [ 50 55 105] [ 60 65 125] [ 70 75 145] [ 80 85 165] [ 90 95 185]]

We may want to predict the value for each of the three time series for the next time step.

This might be referred to as multivariate forecasting.

Again, the data must be split into input/output samples in order to train a model.

The first sample of this dataset would be:

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85

The *split_sequences()* function below will split multiple parallel time series with rows for time steps and one series per column into the required input/output shape.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on the contrived problem; the complete example is listed below.

# multivariate output data prep from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared X and y components.

The shape of X is three-dimensional, including the number of samples (6), the number of time steps chosen per sample (3), and the number of parallel time series or features (3).

The shape of y is two-dimensional as we might expect for the number of samples (6) and the number of time variables per sample to be predicted (3).

The data is ready to use in an LSTM model that expects three-dimensional input and two-dimensional output shapes for the X and y components of each sample.

Then, each of the samples is printed showing the input and output components of each sample.

(6, 3, 3) (6, 3) [[10 15 25] [20 25 45] [30 35 65]] [40 45 85] [[20 25 45] [30 35 65] [40 45 85]] [ 50 55 105] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [ 60 65 125] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [ 70 75 145] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [ 80 85 165] [[ 60 65 125] [ 70 75 145] [ 80 85 165]] [ 90 95 185]

We are now ready to fit an LSTM model on this data.

Any of the varieties of LSTMs in the previous section can be used, such as a Vanilla, Stacked, Bidirectional, CNN, or ConvLSTM model.

We will use a Stacked LSTM where the number of time steps and parallel series (features) are specified for the input layer via the *input_shape* argument. The number of parallel series is also used in the specification of the number of values to predict by the model in the output layer; again, this is three.

# define model model = Sequential() model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps, n_features))) model.add(LSTM(100, activation='relu')) model.add(Dense(n_features)) model.compile(optimizer='adam', loss='mse')

We can predict the next value in each of the three parallel series by providing an input of three time steps for each series.

70, 75, 145 80, 85, 165 90, 95, 185

The shape of the input for making a single prediction must be 1 sample, 3 time steps, and 3 features, or [1, 3, 3]

# demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

We would expect the vector output to be:

[100, 105, 205]

We can tie all of this together and demonstrate a Stacked LSTM for multivariate output time series forecasting below.

# multivariate output stacked lstm example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps, n_features))) model.add(LSTM(100, activation='relu')) model.add(Dense(n_features)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=400, verbose=0) # demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[101.76599 108.730484 206.63577 ]]

A time series forecasting problem that requires a prediction of multiple time steps into the future can be referred to as multi-step time series forecasting.

Specifically, these are problems where the forecast horizon or interval is more than one time step.

There are two main types of LSTM models that can be used for multi-step forecasting; they are:

- Vector Output Model
- Encoder-Decoder Model

Before we look at these models, let’s first look at the preparation of data for multi-step forecasting.

As with one-step forecasting, a time series used for multi-step time series forecasting must be split into samples with input and output components.

Both the input and output components will be comprised of multiple time steps and may or may not have the same number of steps.

For example, given the univariate time series:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We could use the last three time steps as input and forecast the next two time steps.

The first sample would look as follows:

Input:

[10, 20, 30]

Output:

[40, 50]

The *split_sequence()* function below implements this behavior and will split a given univariate time series into samples with a specified number of input and output time steps.

# split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multi-step data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into input and output time steps and prints the input and output components of each.

[10 20 30] [40 50] [20 30 40] [50 60] [30 40 50] [60 70] [40 50 60] [70 80] [50 60 70] [80 90]

Now that we know how to prepare data for multi-step forecasting, let’s look at some LSTM models that can learn this mapping.

Like other types of neural network models, the LSTM can output a vector directly that can be interpreted as a multi-step forecast.

This approach was seen in the previous section were one time step of each output time series was forecasted as a vector.

As with the LSTMs for univariate data in a prior section, the prepared samples must first be reshaped. The LSTM expects data to have a three-dimensional structure of [*samples, timesteps, features*], and in this case, we only have one feature so the reshape is straightforward.

# reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features))

With the number of input and output steps specified in the *n_steps_in* and *n_steps_out* variables, we can define a multi-step time-series forecasting model.

Any of the presented LSTM model types could be used, such as Vanilla, Stacked, Bidirectional, CNN-LSTM, or ConvLSTM. Below defines a Stacked LSTM for multi-step forecasting.

# define model model = Sequential() model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features))) model.add(LSTM(100, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse')

The model can make a prediction for a single sample. We can predict the next two steps beyond the end of the dataset by providing the input:

[70, 80, 90]

We would expect the predicted output to be:

[100, 110]

As expected by the model, the shape of the single sample of input data when making the prediction must be [1, 3, 1] for the 1 sample, 3 time steps of the input, and the single feature.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0)

Tying all of this together, the Stacked LSTM for multi-step forecasting with a univariate time series is listed below.

# univariate multi-step vector-output stacked lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features))) model.add(LSTM(100, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=50, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example forecasts and prints the next two time steps in the sequence.

[[100.98096 113.28924]]

A model specifically developed for forecasting variable length output sequences is called the Encoder-Decoder LSTM.

The model was designed for prediction problems where there are both input and output sequences, so-called sequence-to-sequence, or seq2seq problems, such as translating text from one language to another.

This model can be used for multi-step time series forecasting.

As its name suggests, the model is comprised of two sub-models: the encoder and the decoder.

The encoder is a model responsible for reading and interpreting the input sequence. The output of the encoder is a fixed length vector that represents the model’s interpretation of the sequence. The encoder is traditionally a Vanilla LSTM model, although other encoder models can be used such as Stacked, Bidirectional, and CNN models.

model.add(LSTM(100, activation='relu', input_shape=(n_steps_in, n_features)))

The decoder uses the output of the encoder as an input.

First, the fixed-length output of the encoder is repeated, once for each required time step in the output sequence.

model.add(RepeatVector(n_steps_out))

This sequence is then provided to an LSTM decoder model. The model must output a value for each value in the output time step, which can be interpreted by a single output model.

model.add(LSTM(100, activation='relu', return_sequences=True))

We can use the same output layer or layers to make each one-step prediction in the output sequence. This can be achieved by wrapping the output part of the model in a TimeDistributed wrapper.

model.add(TimeDistributed(Dense(1)))

The full definition for an Encoder-Decoder model for multi-step time series forecasting is listed below.

# define model model = Sequential() model.add(LSTM(100, activation='relu', input_shape=(n_steps_in, n_features))) model.add(RepeatVector(n_steps_out)) model.add(LSTM(100, activation='relu', return_sequences=True)) model.add(TimeDistributed(Dense(1))) model.compile(optimizer='adam', loss='mse')

As with other LSTM models, the input data must be reshaped into the expected three-dimensional shape of [*samples, timesteps, features*].

X = X.reshape((X.shape[0], X.shape[1], n_features))

In the case of the Encoder-Decoder model, the output, or y part, of the training dataset must also have this shape. This is because the model will predict a given number of time steps with a given number of features for each input sample.

y = y.reshape((y.shape[0], y.shape[1], n_features))

The complete example of an Encoder-Decoder LSTM for multi-step time series forecasting is listed below.

# univariate multi-step encoder-decoder lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import RepeatVector from keras.layers import TimeDistributed # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) y = y.reshape((y.shape[0], y.shape[1], n_features)) # define model model = Sequential() model.add(LSTM(100, activation='relu', input_shape=(n_steps_in, n_features))) model.add(RepeatVector(n_steps_out)) model.add(LSTM(100, activation='relu', return_sequences=True)) model.add(TimeDistributed(Dense(1))) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=100, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example forecasts and prints the next two time steps in the sequence.

[[[101.9736 [116.213615]]]

In the previous sections, we have looked at univariate, multivariate, and multi-step time series forecasting.

It is possible to mix and match the different types of LSTM models presented so far for the different problems. This too applies to time series forecasting problems that involve multivariate and multi-step forecasting, but it may be a little more challenging.

In this section, we will provide short examples of data preparation and modeling for multivariate multi-step time series forecasting as a template to ease this challenge, specifically:

- Multiple Input Multi-Step Output.
- Multiple Parallel Input and Multi-Step Output.

Perhaps the biggest stumbling block is in the preparation of data, so this is where we will focus our attention.

There are those multivariate time series forecasting problems where the output series is separate but dependent upon the input time series, and multiple time steps are required for the output series.

For example, consider our multivariate time series from a prior section:

[[ 10 15 25] [ 20 25 45] [ 30 35 65] [ 40 45 85] [ 50 55 105] [ 60 65 125] [ 70 75 145] [ 80 85 165] [ 90 95 185]]

We may use three prior time steps of each of the two input time series to predict two time steps of the output time series.

Input:

10, 15 20, 25 30, 35

Output:

65 85

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on our contrived dataset.

The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # covert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training data.

We can see that the shape of the input portion of the samples is three-dimensional, comprised of six samples, with three time steps, and two variables for the 2 input time series.

The output portion of the samples is two-dimensional for the six samples and the two time steps for each sample to be predicted.

The prepared samples are then printed to confirm that the data was prepared as we specified.

(6, 3, 2) (6, 2) [[10 15] [20 25] [30 35]] [65 85] [[20 25] [30 35] [40 45]] [ 85 105] [[30 35] [40 45] [50 55]] [105 125] [[40 45] [50 55] [60 65]] [125 145] [[50 55] [60 65] [70 75]] [145 165] [[60 65] [70 75] [80 85]] [165 185]

We can now develop an LSTM model for multi-step predictions.

A vector output or an encoder-decoder model could be used. In this case, we will demonstrate a vector output with a Stacked LSTM.

The complete example is listed below.

# multivariate multi-step stacked lstm example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # covert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features))) model.add(LSTM(100, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=200, verbose=0) # demonstrate prediction x_input = array([[70, 75], [80, 85], [90, 95]]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the next two time steps of the output sequence beyond the dataset.

We would expect the next two steps to be: [185, 205]

It is a challenging framing of the problem with very little data, and the arbitrarily configured version of the model gets close.

[[188.70619 210.16513]]

A problem with parallel time series may require the prediction of multiple time steps of each time series.

For example, consider our multivariate time series from a prior section:

We may use the last three time steps from each of the three time series as input to the model and predict the next time steps of each of the three time series as output.

The first sample in the training dataset would be the following.

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85 50, 55, 105

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import RepeatVector from keras.layers import TimeDistributed # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # covert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training dataset.

We can see that both the input (X) and output (Y) elements of the dataset are three dimensional for the number of samples, time steps, and variables or parallel time series respectively.

The input and output elements of each series are then printed side by side so that we can confirm that the data was prepared as we expected.

(5, 3, 3) (5, 2, 3) [[10 15 25] [20 25 45] [30 35 65]] [[ 40 45 85] [ 50 55 105]] [[20 25 45] [30 35 65] [40 45 85]] [[ 50 55 105] [ 60 65 125]] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [[ 60 65 125] [ 70 75 145]] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [[ 70 75 145] [ 80 85 165]] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [[ 80 85 165] [ 90 95 185]]

We can use either the Vector Output or Encoder-Decoder LSTM to model this problem. In this case, we will use the Encoder-Decoder model.

The complete example is listed below.

# multivariate multi-step encoder-decoder lstm example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import RepeatVector from keras.layers import TimeDistributed # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # covert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(LSTM(200, activation='relu', input_shape=(n_steps_in, n_features))) model.add(RepeatVector(n_steps_out)) model.add(LSTM(200, activation='relu', return_sequences=True)) model.add(TimeDistributed(Dense(n_features))) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=300, verbose=0) # demonstrate prediction x_input = array([[60, 65, 125], [70, 75, 145], [80, 85, 165]]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the values for each of the three time steps for the next two time steps beyond the end of the dataset.

We would expect the values for these series and time steps to be as follows:

90, 95, 185 100, 105, 205

We can see that the model forecast gets reasonably close to the expected values.

[[[ 91.86044 97.77231 189.66768 ] [103.299355 109.18123 212.6863 ]]]

In this tutorial, you discovered how to develop a suite of LSTM models for a range of standard time series forecasting problems.

Specifically, you learned:

- How to develop LSTM models for univariate time series forecasting.
- How to develop LSTM models for multivariate time series forecasting.
- How to develop LSTM models for multi-step time series forecasting.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Develop LSTM Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Develop Convolutional Neural Network Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>There are many types of CNN models that can be used for each specific type of time series forecasting problem.

In this tutorial, you will discover how to develop a suite of CNN models for a range of standard time series forecasting problems.

The objective of this tutorial is to provide standalone examples of each model on each type of time series problem as a template that you can copy and adapt for your specific time series forecasting problem.

After completing this tutorial, you will know:

- How to develop CNN models for univariate time series forecasting.
- How to develop CNN models for multivariate time series forecasting.
- How to develop CNN models for multi-step time series forecasting.

This is a large and important post; you may want to bookmark it for future reference.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new book, with 25 step-by-step tutorials and full source code.

Let’s get started.

In this tutorial, we will explore how to develop a suite of different types of CNN models for time series forecasting.

The models are demonstrated on small contrived time series problems intended to give the flavor of the type of time series problem being addressed. The chosen configuration of the models is arbitrary and not optimized for each problem; that was not the goal.

This tutorial is divided into four parts; they are:

- Univariate CNN Models
- Multivariate CNN Models
- Multi-Step CNN Models
- Multivariate Multi-Step CNN Models

Although traditionally developed for two-dimensional image data, CNNs can be used to model univariate time series forecasting problems.

Univariate time series are datasets comprised of a single series of observations with a temporal ordering and a model is required to learn from the series of past observations to predict the next value in the sequence.

This section is divided into two parts; they are:

- Data Preparation
- CNN Model

Before a univariate series can be modeled, it must be prepared.

The CNN model will learn a function that maps a sequence of past observations as input to an output observation. As such, the sequence of observations must be transformed into multiple examples from which the model can learn.

Consider a given univariate sequence:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We can divide the sequence into multiple input/output patterns called samples, where three time steps are used as input and one time step is used as output for the one-step prediction that is being learned.

X, y 10, 20, 30 40 20, 30, 40 50 30, 40, 50 60 ...

The *split_sequence()* function below implements this behavior and will split a given univariate sequence into multiple samples where each sample has a specified number of time steps and the output is a single time step.

# split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on our small contrived dataset above.

The complete example is listed below.

# univariate data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into six samples where each sample has three input time steps and one output time step.

[10 20 30] 40 [20 30 40] 50 [30 40 50] 60 [40 50 60] 70 [50 60 70] 80 [60 70 80] 90

Now that we know how to prepare a univariate series for modeling, let’s look at developing a CNN model that can learn the mapping of inputs to outputs.

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A one-dimensional CNN is a CNN model that has a convolutional hidden layer that operates over a 1D sequence. This is followed by perhaps a second convolutional layer in some cases, such as very long input sequences, and then a pooling layer whose job it is to distill the output of the convolutional layer to the most salient elements.

The convolutional and pooling layers are followed by a dense fully connected layer that interprets the features extracted by the convolutional part of the model. A flatten layer is used between the convolutional layers and the dense layer to reduce the feature maps to a single one-dimensional vector.

We can define a 1D CNN Model for univariate time series forecasting as follows.

# define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

Key in the definition is the shape of the input; that is what the model expects as input for each sample in terms of the number of time steps and the number of features.

We are working with a univariate series, so the number of features is one, for one variable.

The number of time steps as input is the number we chose when preparing our dataset as an argument to the *split_sequence()* function.

The input shape for each sample is specified in the *input_shape* argument on the definition of the first hidden layer.

We almost always have multiple samples, therefore, the model will expect the input component of training data to have the dimensions or shape:

[samples, timesteps, features]

Our *split_sequence()* function in the previous section outputs the X with the shape [*samples, timesteps*], so we can easily reshape it to have an additional dimension for the one feature.

# reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features))

The CNN does not actually view the data as having time steps, instead, it is treated as a sequence over which convolutional read operations can be performed, like a one-dimensional image.

In this example, we define a convolutional layer with 64 filter maps and a kernel size of 2. This is followed by a max pooling layer and a dense layer to interpret the input feature. An output layer is specified that predicts a single numerical value.

The model is fit using the efficient Adam version of stochastic gradient descent and optimized using the mean squared error, or ‘*mse*‘, loss function.

Once the model is defined, we can fit it on the training dataset.

# fit model model.fit(X, y, epochs=1000, verbose=0)

After the model is fit, we can use it to make a prediction.

We can predict the next value in the sequence by providing the input:

[70, 80, 90]

And expecting the model to predict something like:

[100]

The model expects the input shape to be three-dimensional with [*samples, timesteps, features*], therefore, we must reshape the single input sample before making the prediction.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

We can tie all of this together and demonstrate how to develop a 1D CNN model for univariate time series forecasting and make a single prediction.

# univariate cnn example from numpy import array from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=1000, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

Your results may vary given the stochastic nature of the algorithm; try running the example a few times.

We can see that the model predicts the next value in the sequence.

[[101.67965]]

Multivariate time series data means data where there is more than one observation for each time step.

There are two main models that we may require with multivariate time series data; they are:

- Multiple Input Series.
- Multiple Parallel Series.

Let’s take a look at each in turn.

A problem may have two or more parallel input time series and an output time series that is dependent on the input time series.

The input time series are parallel because each series has observations at the same time steps.

We can demonstrate this with a simple example of two parallel input time series where the output series is the simple addition of the input series.

# define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])

We can reshape these three arrays of data as a single dataset where each row is a time step and each column is a separate time series.

This is a standard way of storing parallel time series in a CSV file.

# convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq))

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) print(dataset)

Running the example prints the dataset with one row per time step and one column for each of the two input and one output parallel time series.

As with the univariate time series, we must structure these data into samples with input and output samples.

A 1D CNN model needs sufficient context to learn a mapping from an input sequence to an output value. CNNs can support parallel input time series as separate channels, like red, green, and blue components of an image. Therefore, we need to split the data into samples maintaining the order of observations across the two input sequences.

If we chose three input time steps, then the first sample would look as follows:

Input:

10, 15 20, 25 30, 35

Output:

65

That is, the first three time steps of each parallel series are provided as input to the model and the model associates this with the value in the output series at the third time step, in this case, 65.

We can see that, in transforming the time series into input/output samples to train the model, that we will have to discard some values from the output time series where we do not have values in the input time series at prior time steps. In turn, the choice of the size of the number of input time steps will have an important effect on how much of the training data is used.

We can define a function named *split_sequences()* that will take a dataset as we have defined it with rows for time steps and columns for parallel series and return input/output samples.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can test this function on our dataset using three time steps for each input time series as input.

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the *X* and *y* components.

We can see that the *X* component has a three-dimensional structure.

The first dimension is the number of samples, in this case 7. The second dimension is the number of time steps per sample, in this case 3, the value specified to the function. Finally, the last dimension specifies the number of parallel time series or the number of variables, in this case 2 for the two parallel series.

This is the exact three-dimensional structure expected by a 1D CNN as input. The data is ready to use without further reshaping.

We can then see that the input and output for each sample is printed, showing the three time steps for each of the two input series and the associated output for each sample.

(7, 3, 2) (7,) [[10 15] [20 25] [30 35]] 65 [[20 25] [30 35] [40 45]] 85 [[30 35] [40 45] [50 55]] 105 [[40 45] [50 55] [60 65]] 125 [[50 55] [60 65] [70 75]] 145 [[60 65] [70 75] [80 85]] 165 [[70 75] [80 85] [90 95]] 185

We are now ready to fit a 1D CNN model on this data, specifying the expected number of time steps and features to expect for each input sample, in this case three and two respectively.

# define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

When making a prediction, the model expects three time steps for two input time series.

We can predict the next value in the output series providing the input values of:

80, 85 90, 95 100, 105

The shape of the one sample with three time steps and two variables must be [1, 3, 2].

We would expect the next value in the sequence to be 100 + 105 or 205.

# demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

The complete example is listed below.

# multivariate cnn example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=1000, verbose=0) # demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[206.0161]]

There is another, more elaborate way to model the problem.

Each input series can be handled by a separate CNN and the output of each of these submodels can be combined before a prediction is made for the output sequence.

We can refer to this as a multi-headed CNN model. It may offer more flexibility or better performance depending on the specifics of the problem that is being modeled. For example, it allows you to configure each sub-model differently for each input series, such as the number of filter maps and the kernel size.

This type of model can be defined in Keras using the Keras functional API.

First, we can define the first input model as a 1D CNN with an input layer that expects vectors with *n_steps* and 1 feature.

# first input model visible1 = Input(shape=(n_steps, n_features)) cnn1 = Conv1D(filters=64, kernel_size=2, activation='relu')(visible1) cnn1 = MaxPooling1D(pool_size=2)(cnn1) cnn1 = Flatten()(cnn1)

We can define the second input submodel in the same way.

# second input model visible2 = Input(shape=(n_steps, n_features)) cnn2 = Conv1D(filters=64, kernel_size=2, activation='relu')(visible2) cnn2 = MaxPooling1D(pool_size=2)(cnn2) cnn2 = Flatten()(cnn2)

Now that both input submodels have been defined, we can merge the output from each model into one long vector which can be interpreted before making a prediction for the output sequence.

# merge input models merge = concatenate([cnn1, cnn2]) dense = Dense(50, activation='relu')(merge) output = Dense(1)(dense)

We can then tie the inputs and outputs together.

model = Model(inputs=[visible1, visible2], outputs=output)

The image below provides a schematic for how this model looks, including the shape of the inputs and outputs of each layer.

This model requires input to be provided as a list of two elements where each element in the list contains data for one of the submodels.

In order to achieve this, we can split the 3D input data into two separate arrays of input data; that is from one array with the shape [7, 3, 2] to two 3D arrays with [7, 3, 1]

# one time series per head n_features = 1 # separate input data X1 = X[:, :, 0].reshape(X.shape[0], X.shape[1], n_features) X2 = X[:, :, 1].reshape(X.shape[0], X.shape[1], n_features)

These data can then be provided in order to fit the model.

# fit model model.fit([X1, X2], y, epochs=1000, verbose=0)

Similarly, we must prepare the data for a single sample as two separate two-dimensional arrays when making a single one-step prediction.

x_input = array([[80, 85], [90, 95], [100, 105]]) x1 = x_input[:, 0].reshape((1, n_steps, n_features)) x2 = x_input[:, 1].reshape((1, n_steps, n_features))

We can tie all of this together; the complete example is listed below.

# multivariate multi-headed 1d cnn example from numpy import array from numpy import hstack from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D from keras.layers.merge import concatenate # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # one time series per head n_features = 1 # separate input data X1 = X[:, :, 0].reshape(X.shape[0], X.shape[1], n_features) X2 = X[:, :, 1].reshape(X.shape[0], X.shape[1], n_features) # first input model visible1 = Input(shape=(n_steps, n_features)) cnn1 = Conv1D(filters=64, kernel_size=2, activation='relu')(visible1) cnn1 = MaxPooling1D(pool_size=2)(cnn1) cnn1 = Flatten()(cnn1) # second input model visible2 = Input(shape=(n_steps, n_features)) cnn2 = Conv1D(filters=64, kernel_size=2, activation='relu')(visible2) cnn2 = MaxPooling1D(pool_size=2)(cnn2) cnn2 = Flatten()(cnn2) # merge input models merge = concatenate([cnn1, cnn2]) dense = Dense(50, activation='relu')(merge) output = Dense(1)(dense) model = Model(inputs=[visible1, visible2], outputs=output) model.compile(optimizer='adam', loss='mse') # fit model model.fit([X1, X2], y, epochs=1000, verbose=0) # demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x1 = x_input[:, 0].reshape((1, n_steps, n_features)) x2 = x_input[:, 1].reshape((1, n_steps, n_features)) yhat = model.predict([x1, x2], verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[205.871]]

An alternate time series problem is the case where there are multiple parallel time series and a value must be predicted for each.

For example, given the data from the previous section:

We may want to predict the value for each of the three time series for the next time step.

This might be referred to as multivariate forecasting.

Again, the data must be split into input/output samples in order to train a model.

The first sample of this dataset would be:

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85

The *split_sequences()* function below will split multiple parallel time series with rows for time steps and one series per column into the required input/output shape.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on the contrived problem; the complete example is listed below.

# multivariate output data prep from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared X and y components.

The shape of X is three-dimensional, including the number of samples (6), the number of time steps chosen per sample (3), and the number of parallel time series or features (3).

The shape of y is two-dimensional as we might expect for the number of samples (6) and the number of time variables per sample to be predicted (3).

The data is ready to use in a 1D CNN model that expects three-dimensional input and two-dimensional output shapes for the X and y components of each sample.

Then, each of the samples is printed showing the input and output components of each sample.

(6, 3, 3) (6, 3) [[10 15 25] [20 25 45] [30 35 65]] [40 45 85] [[20 25 45] [30 35 65] [40 45 85]] [ 50 55 105] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [ 60 65 125] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [ 70 75 145] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [ 80 85 165] [[ 60 65 125] [ 70 75 145] [ 80 85 165]] [ 90 95 185]

We are now ready to fit a 1D CNN model on this data.

In this model, the number of time steps and parallel series (features) are specified for the input layer via the *input_shape* argument.

The number of parallel series is also used in the specification of the number of values to predict by the model in the output layer; again, this is three.

# define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_features)) model.compile(optimizer='adam', loss='mse')

We can predict the next value in each of the three parallel series by providing an input of three time steps for each series.

70, 75, 145 80, 85, 165 90, 95, 185

The shape of the input for making a single prediction must be 1 sample, 3 time steps, and 3 features, or [1, 3, 3].

# demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0)

We would expect the vector output to be:

[100, 105, 205]

We can tie all of this together and demonstrate a 1D CNN for multivariate output time series forecasting below.

# multivariate output 1d cnn example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_features)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=3000, verbose=0) # demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model and makes a prediction.

[[100.11272 105.32213 205.53436]]

As with multiple input series, there is another more elaborate way to model the problem.

Each output series can be handled by a separate output CNN model.

We can refer to this as a multi-output CNN model. It may offer more flexibility or better performance depending on the specifics of the problem that is being modeled.

This type of model can be defined in Keras using the Keras functional API.

First, we can define the first input model as a 1D CNN model.

# define model visible = Input(shape=(n_steps, n_features)) cnn = Conv1D(filters=64, kernel_size=2, activation='relu')(visible) cnn = MaxPooling1D(pool_size=2)(cnn) cnn = Flatten()(cnn) cnn = Dense(50, activation='relu')(cnn)

We can then define one output layer for each of the three series that we wish to forecast, where each output submodel will forecast a single time step.

# define output 1 output1 = Dense(1)(cnn) # define output 2 output2 = Dense(1)(cnn) # define output 3 output3 = Dense(1)(cnn)

We can then tie the input and output layers together into a single model.

# tie together model = Model(inputs=visible, outputs=[output1, output2, output3]) model.compile(optimizer='adam', loss='mse')

To make the model architecture clear, the schematic below clearly shows the three separate output layers of the model and the input and output shapes of each layer.

When training the model, it will require three separate output arrays per sample. We can achieve this by converting the output training data that has the shape [7, 3] to three arrays with the shape [7, 1].

# separate output y1 = y[:, 0].reshape((y.shape[0], 1)) y2 = y[:, 1].reshape((y.shape[0], 1)) y3 = y[:, 2].reshape((y.shape[0], 1))

These arrays can be provided to the model during training.

# fit model model.fit(X, [y1,y2,y3], epochs=2000, verbose=0)

Tying all of this together, the complete example is listed below.

# multivariate output 1d cnn example from numpy import array from numpy import hstack from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # separate output y1 = y[:, 0].reshape((y.shape[0], 1)) y2 = y[:, 1].reshape((y.shape[0], 1)) y3 = y[:, 2].reshape((y.shape[0], 1)) # define model visible = Input(shape=(n_steps, n_features)) cnn = Conv1D(filters=64, kernel_size=2, activation='relu')(visible) cnn = MaxPooling1D(pool_size=2)(cnn) cnn = Flatten()(cnn) cnn = Dense(50, activation='relu')(cnn) # define output 1 output1 = Dense(1)(cnn) # define output 2 output2 = Dense(1)(cnn) # define output 3 output3 = Dense(1)(cnn) # tie together model = Model(inputs=visible, outputs=[output1, output2, output3]) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, [y1,y2,y3], epochs=2000, verbose=0) # demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_steps, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[array([[100.96118]], dtype=float32), array([[105.502686]], dtype=float32), array([[205.98045]], dtype=float32)]

In practice, there is little difference to the 1D CNN model in predicting a vector output that represents different output variables (as in the previous example), or a vector output that represents multiple time steps of one variable.

Nevertheless, there are subtle and important differences in the way the training data is prepared. In this section, we will demonstrate the case of developing a multi-step forecast model using a vector model.

Before we look at the specifics of the model, let’s first look at the preparation of data for multi-step forecasting.

As with one-step forecasting, a time series used for multi-step time series forecasting must be split into samples with input and output components.

Both the input and output components will be comprised of multiple time steps and may or may not have the same number of steps.

For example, given the univariate time series:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We could use the last three time steps as input and forecast the next two time steps.

The first sample would look as follows:

Input:

[10, 20, 30]

Output:

[40, 50]

The *split_sequence()* function below implements this behavior and will split a given univariate time series into samples with a specified number of input and output time steps.

# split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multi-step data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into input and output time steps and prints the input and output components of each.

[10 20 30] [40 50] [20 30 40] [50 60] [30 40 50] [60 70] [40 50 60] [70 80] [50 60 70] [80 90]

Now that we know how to prepare data for multi-step forecasting, let’s look at a 1D CNN model that can learn this mapping.

The 1D CNN can output a vector directly that can be interpreted as a multi-step forecast.

This approach was seen in the previous section were one time step of each output time series was forecasted as a vector.

As with the 1D CNN models for univariate data in a prior section, the prepared samples must first be reshaped. The CNN expects data to have a three-dimensional structure of [*samples, timesteps, features*], and in this case, we only have one feature so the reshape is straightforward.

With the number of input and output steps specified in the *n_steps_in* and *n_steps_out* variables, we can define a multi-step time-series forecasting model.

# define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse')

The model can make a prediction for a single sample. We can predict the next two steps beyond the end of the dataset by providing the input:

[70, 80, 90]

We would expect the predicted output to be:

[100, 110]

As expected by the model, the shape of the single sample of input data when making the prediction must be [1, 3, 1] for the 1 sample, 3 time steps of the input, and the single feature.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0)

Tying all of this together, the 1D CNN for multi-step forecasting with a univariate time series is listed below.

# univariate multi-step vector-output 1d cnn example from numpy import array from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # reshape from [samples, timesteps] into [samples, timesteps, features] n_features = 1 X = X.reshape((X.shape[0], X.shape[1], n_features)) # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example forecasts and prints the next two time steps in the sequence.

[[102.86651 115.08979]]

In the previous sections, we have looked at univariate, multivariate, and multi-step time series forecasting.

It is possible to mix and match the different types of 1D CNN models presented so far for the different problems. This too applies to time series forecasting problems that involve multivariate and multi-step forecasting, but it may be a little more challenging.

In this section, we will explore short examples of data preparation and modeling for multivariate multi-step time series forecasting as a template to ease this challenge, specifically:

- Multiple Input Multi-Step Output.
- Multiple Parallel Input and Multi-Step Output.

Perhaps the biggest stumbling block is in the preparation of data, so this is where we will focus our attention.

There are those multivariate time series forecasting problems where the output series is separate but dependent upon the input time series, and multiple time steps are required for the output series.

For example, consider our multivariate time series from a prior section:

We may use three prior time steps of each of the two input time series to predict two time steps of the output time series.

Input:

10, 15 20, 25 30, 35

Output:

65 85

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on our contrived dataset. The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training data.

We can see that the shape of the input portion of the samples is three-dimensional, comprised of six samples, with three time steps and two variables for the two input time series.

The output portion of the samples is two-dimensional for the six samples and the two time steps for each sample to be predicted.

The prepared samples are then printed to confirm that the data was prepared as we specified.

(6, 3, 2) (6, 2) [[10 15] [20 25] [30 35]] [65 85] [[20 25] [30 35] [40 45]] [ 85 105] [[30 35] [40 45] [50 55]] [105 125] [[40 45] [50 55] [60 65]] [125 145] [[50 55] [60 65] [70 75]] [145 165] [[60 65] [70 75] [80 85]] [165 185]

We can now develop a 1D CNN model for multi-step predictions.

In this case, we will demonstrate a vector output model. The complete example is listed below.

# multivariate multi-step 1d cnn example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[70, 75], [80, 85], [90, 95]]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the next two time steps of the output sequence beyond the dataset.

We would expect the next two steps to be [185, 205].

It is a challenging framing of the problem with very little data, and the arbitrarily configured version of the model gets close.

[[185.57011 207.77893]]

A problem with parallel time series may require the prediction of multiple time steps of each time series.

For example, consider our multivariate time series from a prior section:

We may use the last three time steps from each of the three time series as input to the model, and predict the next time steps of each of the three time series as output.

The first sample in the training dataset would be the following.

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85 50, 55, 105

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import RepeatVector from keras.layers import TimeDistributed # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training dataset.

We can see that both the input (*X*) and output (*Y*) elements of the dataset are three dimensional for the number of samples, time steps, and variables or parallel time series respectively.

The input and output elements of each series are then printed side by side so that we can confirm that the data was prepared as we expected.

(5, 3, 3) (5, 2, 3) [[10 15 25] [20 25 45] [30 35 65]] [[ 40 45 85] [ 50 55 105]] [[20 25 45] [30 35 65] [40 45 85]] [[ 50 55 105] [ 60 65 125]] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [[ 60 65 125] [ 70 75 145]] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [[ 70 75 145] [ 80 85 165]] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [[ 80 85 165] [ 90 95 185]]

We can now develop a 1D CNN model for this dataset.

We will use a vector-output model in this case. As such, we must flatten the three-dimensional structure of the output portion of each sample in order to train the model. This means, instead of predicting two steps for each series, the model is trained on and expected to predict a vector of six numbers directly.

# flatten output n_output = y.shape[1] * y.shape[2] y = y.reshape((y.shape[0], n_output))

The complete example is listed below.

# multivariate output multi-step 1d cnn example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # flatten output n_output = y.shape[1] * y.shape[2] y = y.reshape((y.shape[0], n_output)) # the dataset knows the number of features, e.g. 2 n_features = X.shape[2] # define model model = Sequential() model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dense(n_output)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=7000, verbose=0) # demonstrate prediction x_input = array([[60, 65, 125], [70, 75, 145], [80, 85, 165]]) x_input = x_input.reshape((1, n_steps_in, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the values for each of the three time steps for the next two time steps beyond the end of the dataset.

We would expect the values for these series and time steps to be as follows:

90, 95, 185 100, 105, 205

We can see that the model forecast gets reasonably close to the expected values.

[[ 90.47855 95.621284 186.02629 100.48118 105.80815 206.52821 ]]

In this tutorial, you discovered how to develop a suite of CNN models for a range of standard time series forecasting problems.

Specifically, you learned:

- How to develop CNN models for univariate time series forecasting.
- How to develop CNN models for multivariate time series forecasting.
- How to develop CNN models for multi-step time series forecasting.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Develop Convolutional Neural Network Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Develop Multilayer Perceptron Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>A challenge with using MLPs for time series forecasting is in the preparation of the data. Specifically, lag observations must be flattened into feature vectors.

In this tutorial, you will discover how to develop a suite of MLP models for a range of standard time series forecasting problems.

The objective of this tutorial is to provide standalone examples of each model on each type of time series problem as a template that you can copy and adapt for your specific time series forecasting problem.

In this tutorial, you will discover how to develop a suite of Multilayer Perceptron models for a range of standard time series forecasting problems.

After completing this tutorial, you will know:

- How to develop MLP models for univariate time series forecasting.
- How to develop MLP models for multivariate time series forecasting.
- How to develop MLP models for multi-step time series forecasting.

Let’s get started.

This tutorial is divided into four parts; they are:

- Univariate MLP Models
- Multivariate MLP Models
- Multi-Step MLP Models
- Multivariate Multi-Step MLP Models

Multilayer Perceptrons, or MLPs for short, can be used to model univariate time series forecasting problems.

Univariate time series are a dataset comprised of a single series of observations with a temporal ordering and a model is required to learn from the series of past observations to predict the next value in the sequence.

This section is divided into two parts; they are:

- Data Preparation
- MLP Model

Before a univariate series can be modeled, it must be prepared.

The MLP model will learn a function that maps a sequence of past observations as input to an output observation. As such, the sequence of observations must be transformed into multiple examples from which the model can learn.

Consider a given univariate sequence:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We can divide the sequence into multiple input/output patterns called samples, where three time steps are used as input and one time step is used as output for the one-step prediction that is being learned.

X, y 10, 20, 30 40 20, 30, 40 50 30, 40, 50 60 ...

The *split_sequence()* function below implements this behavior and will split a given univariate sequence into multiple samples where each sample has a specified number of time steps and the output is a single time step.

# split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on our small contrived dataset above.

The complete example is listed below.

# univariate data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into six samples where each sample has three input time steps and one output time step.

[10 20 30] 40 [20 30 40] 50 [30 40 50] 60 [40 50 60] 70 [50 60 70] 80 [60 70 80] 90

Now that we know how to prepare a univariate series for modeling, let’s look at developing an MLP model that can learn the mapping of inputs to outputs.

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A simple MLP model has a single hidden layer of nodes, and an output layer used to make a prediction.

We can define an MLP for univariate time series forecasting as follows.

# define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_steps)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

Important in the definition is the shape of the input; that is what the model expects as input for each sample in terms of the number of time steps.

The number of time steps as input is the number we chose when preparing our dataset as an argument to the *split_sequence()* function.

The input dimension for each sample is specified in the *input_dim* argument on the definition of first hidden layer. Technically, the model will view each time step as a separate feature instead of separate time steps.

We almost always have multiple samples, therefore, the model will expect the input component of training data to have the dimensions or shape:

[samples, features]

Our *split_sequence()* function in the previous section outputs the *X* with the shape *[samples, features]* ready to use for modeling.

The model is fit using the efficient Adam version of stochastic gradient descent and optimized using the mean squared error, or ‘*mse*‘, loss function.

Once the model is defined, we can fit it on the training dataset.

# fit model model.fit(X, y, epochs=2000, verbose=0)

After the model is fit, we can use it to make a prediction.

We can predict the next value in the sequence by providing the input:

[70, 80, 90]

And expecting the model to predict something like:

[100]

The model expects the input shape to be two-dimensional with *[samples, features]*, therefore, we must reshape the single input sample before making the prediction, e.g with the shape [1, 3] for 1 sample and 3 time steps used as input features.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps)) yhat = model.predict(x_input, verbose=0)

We can tie all of this together and demonstrate how to develop an MLP for univariate time series forecasting and make a single prediction.

# univariate mlp example from numpy import array from keras.models import Sequential from keras.layers import Dense # split a univariate sequence into samples def split_sequence(sequence, n_steps): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the sequence if end_ix > len(sequence)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps = 3 # split into samples X, y = split_sequence(raw_seq, n_steps) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_steps)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

Your results may vary given the stochastic nature of the algorithm; try running the example a few times.

We can see that the model predicts the next value in the sequence.

[[100.0109]]

Multivariate time series data means data where there is more than one observation for each time step.

There are two main models that we may require with multivariate time series data; they are:

- Multiple Input Series.
- Multiple Parallel Series.

Let’s take a look at each in turn.

A problem may have two or more parallel input time series and an output time series that is dependent on the input time series.

The input time series are parallel because each series has an observation at the same time step.

We can demonstrate this with a simple example of two parallel input time series where the output series is the simple addition of the input series.

# define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])

We can reshape these three arrays of data as a single dataset where each row is a time step and each column is a separate time series. This is a standard way of storing parallel time series in a CSV file.

# convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq))

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) print(dataset)

Running the example prints the dataset with one row per time step and one column for each of the two input and one output parallel time series.

As with the univariate time series, we must structure these data into samples with input and output samples.

We need to split the data into samples maintaining the order of observations across the two input sequences.

If we chose three input time steps, then the first sample would look as follows:

Input:

10, 15 20, 25 30, 35

Output:

65

That is, the first three time steps of each parallel series are provided as input to the model and the model associates this with the value in the output series at the third time step, in this case 65.

We can see that, in transforming the time series into input/output samples to train the model, that we will have to discard some values from the output time series where we do not have values in the input time series at prior time steps. In turn, the choice of the size of the number of input time steps will have an important effect on how much of the training data is used.

We can define a function named *split_sequences()* that will take a dataset as we have defined it with rows for time steps and columns for parallel series and return input/output samples.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can test this function on our dataset using three time steps for each input time series as input.

The complete example is listed below.

# multivariate data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the X and y components.

We can see that the X component has a three-dimensional structure.

The first dimension is the number of samples, in this case 7. The second dimension is the number of time steps per sample, in this case 3, the value specified to the function. Finally, the last dimension specifies the number of parallel time series or the number of variables, in this case 2 for the two parallel series.

We can then see that the input and output for each sample is printed, showing the three time steps for each of the two input series and the associated output for each sample.

(7, 3, 2) (7,) [[10 15] [20 25] [30 35]] 65 [[20 25] [30 35] [40 45]] 85 [[30 35] [40 45] [50 55]] 105 [[40 45] [50 55] [60 65]] 125 [[50 55] [60 65] [70 75]] 145 [[60 65] [70 75] [80 85]] 165 [[70 75] [80 85] [90 95]] 185

Before we can fit an MLP on this data, we must flatten the shape of the input samples.

MLPs require that the shape of the input portion of each sample is a vector. With a multivariate input, we will have multiple vectors, one for each time step.

We can flatten the temporal structure of each input sample, so that:

[[10 15] [20 25] [30 35]]

Becomes:

[10, 15, 20, 25, 30, 35]

First, we can calculate the length of each input vector as the number of time steps multiplied by the number of features or time series. We can then use this vector size to reshape the input.

# flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input))

We can now define an MLP model for the multivariate input where the vector length is used for the input dimension argument.

# define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

When making a prediction, the model expects three time steps for two input time series.

We can predict the next value in the output series proving the input values of:

80, 85 90, 95 100, 105

The shape of the 1 sample with 3 time steps and 2 variables would be [1, 3, 2]. We must again reshape this to be 1 sample with a vector of 6 elements or [1, 6]

We would expect the next value in the sequence to be 100 + 105 or 205.

# demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0)

The complete example is listed below.

# multivariate mlp example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[205.04436]]

There is another more elaborate way to model the problem.

Each input series can be handled by a separate MLP and the output of each of these submodels can be combined before a prediction is made for the output sequence.

We can refer to this as a multi-headed input MLP model. It may offer more flexibility or better performance depending on the specifics of the problem that are being modeled.

This type of model can be defined in Keras using the Keras functional API.

First, we can define the first input model as an MLP with an input layer that expects vectors with *n_steps* features.

# first input model visible1 = Input(shape=(n_steps,)) dense1 = Dense(100, activation='relu')(visible1)

We can define the second input submodel in the same way.

# second input model visible2 = Input(shape=(n_steps,)) dense2 = Dense(100, activation='relu')(visible2)

Now that both input submodels have been defined, we can merge the output from each model into one long vector, which can be interpreted before making a prediction for the output sequence.

# merge input models merge = concatenate([dense1, dense2]) output = Dense(1)(merge)

We can then tie the inputs and outputs together.

model = Model(inputs=[visible1, visible2], outputs=output)

The image below provides a schematic for how this model looks, including the shape of the inputs and outputs of each layer.

This model requires input to be provided as a list of two elements, where each element in the list contains data for one of the submodels.

In order to achieve this, we can split the 3D input data into two separate arrays of input data: that is from one array with the shape [7, 3, 2] to two 2D arrays with the shape [7, 3]

# separate input data X1 = X[:, :, 0] X2 = X[:, :, 1]

These data can then be provided in order to fit the model.

# fit model model.fit([X1, X2], y, epochs=2000, verbose=0)

Similarly, we must prepare the data for a single sample as two separate two-dimensional arrays when making a single one-step prediction.

x_input = array([[80, 85], [90, 95], [100, 105]]) x1 = x_input[:, 0].reshape((1, n_steps)) x2 = x_input[:, 1].reshape((1, n_steps))

We can tie all of this together; the complete example is listed below.

# multivariate mlp example from numpy import array from numpy import hstack from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers.merge import concatenate # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # separate input data X1 = X[:, :, 0] X2 = X[:, :, 1] # first input model visible1 = Input(shape=(n_steps,)) dense1 = Dense(100, activation='relu')(visible1) # second input model visible2 = Input(shape=(n_steps,)) dense2 = Dense(100, activation='relu')(visible2) # merge input models merge = concatenate([dense1, dense2]) output = Dense(1)(merge) model = Model(inputs=[visible1, visible2], outputs=output) model.compile(optimizer='adam', loss='mse') # fit model model.fit([X1, X2], y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[80, 85], [90, 95], [100, 105]]) x1 = x_input[:, 0].reshape((1, n_steps)) x2 = x_input[:, 1].reshape((1, n_steps)) yhat = model.predict([x1, x2], verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[206.05022]]

An alternate time series problem is the case where there are multiple parallel time series and a value must be predicted for each.

For example, given the data from the previous section:

We may want to predict the value for each of the three time series for the next time step.

This might be referred to as multivariate forecasting.

Again, the data must be split into input/output samples in order to train a model.

The first sample of this dataset would be:

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85

The *split_sequences()* function below will split multiple parallel time series with rows for time steps and one series per column into the required input/output shape.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on the contrived problem; the complete example is listed below.

# multivariate output data prep from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared *X* and *y* components.

The shape of *X* is three-dimensional, including the number of samples (6), the number of time steps chosen per sample (3), and the number of parallel time series or features (3).

The shape of *y* is two-dimensional as we might expect for the number of samples (6) and the number of time variables per sample to be predicted (3).

Then, each of the samples is printed showing the input and output components of each sample.

(6, 3, 3) (6, 3) [[10 15 25] [20 25 45] [30 35 65]] [40 45 85] [[20 25 45] [30 35 65] [40 45 85]] [ 50 55 105] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [ 60 65 125] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [ 70 75 145] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [ 80 85 165] [[ 60 65 125] [ 70 75 145] [ 80 85 165]] [ 90 95 185]

We are now ready to fit an MLP model on this data.

As with the previous case of multivariate input, we must flatten the three dimensional structure of the input data samples to a two dimensional structure of [*samples, features*], where lag observations are treated as features by the model.

# flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input))

The model output will be a vector, with one element for each of the three different time series.

n_output = y.shape[1]

We can now define our model, using the flattened vector length for the input layer and the number of time series as the vector length when making a prediction.

# define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(n_output)) model.compile(optimizer='adam', loss='mse')

We can predict the next value in each of the three parallel series by providing an input of three time steps for each series.

70, 75, 145 80, 85, 165 90, 95, 185

The shape of the input for making a single prediction must be 1 sample, 3 time steps and 3 features, or [1, 3, 3]. Again, we can flatten this to [1, 6] to meet the expectations of the model.

We would expect the vector output to be:

[100, 105, 205]

# demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0)

We can tie all of this together and demonstrate an MLP for multivariate output time series forecasting below.

# multivariate output mlp example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) n_output = y.shape[1] # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(n_output)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[[100.95039 107.541306 206.81033 ]]

As with multiple input series, there is another, more elaborate way to model the problem.

Each output series can be handled by a separate output MLP model.

We can refer to this as a multi-output MLP model. It may offer more flexibility or better performance depending on the specifics of the problem that is being modeled.

This type of model can be defined in Keras using the Keras functional API.

First, we can define the input model as an MLP with an input layer that expects flattened feature vectors.

# define model visible = Input(shape=(n_input,)) dense = Dense(100, activation='relu')(visible)

We can then define one output layer for each of the three series that we wish to forecast, where each output submodel will forecast a single time step.

# define output 1 output1 = Dense(1)(dense) # define output 2 output2 = Dense(1)(dense) # define output 2 output3 = Dense(1)(dense)

We can then tie the input and output layers together into a single model.

# tie together model = Model(inputs=visible, outputs=[output1, output2, output3]) model.compile(optimizer='adam', loss='mse')

To make the model architecture clear, the schematic below clearly shows the three separate output layers of the model and the input and output shapes of each layer.

When training the model, it will require three separate output arrays per sample.

We can achieve this by converting the output training data that has the shape [7, 3] to three arrays with the shape [7, 1].

# separate output y1 = y[:, 0].reshape((y.shape[0], 1)) y2 = y[:, 1].reshape((y.shape[0], 1)) y3 = y[:, 2].reshape((y.shape[0], 1))

These arrays can be provided to the model during training.

# fit model model.fit(X, [y1,y2,y3], epochs=2000, verbose=0)

Tying all of this together, the complete example is listed below.

# multivariate output mlp example from numpy import array from numpy import hstack from keras.models import Model from keras.layers import Input from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps # check if we are beyond the dataset if end_ix > len(sequences)-1: break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps = 3 # convert into input/output X, y = split_sequences(dataset, n_steps) # flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) # separate output y1 = y[:, 0].reshape((y.shape[0], 1)) y2 = y[:, 1].reshape((y.shape[0], 1)) y3 = y[:, 2].reshape((y.shape[0], 1)) # define model visible = Input(shape=(n_input,)) dense = Dense(100, activation='relu')(visible) # define output 1 output1 = Dense(1)(dense) # define output 2 output2 = Dense(1)(dense) # define output 2 output3 = Dense(1)(dense) # tie together model = Model(inputs=visible, outputs=[output1, output2, output3]) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, [y1,y2,y3], epochs=2000, verbose=0) # demonstrate prediction x_input = array([[70,75,145], [80,85,165], [90,95,185]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction.

[array([[100.86121]], dtype=float32), array([[105.14738]], dtype=float32), array([[205.97507]], dtype=float32)]

In practice, there is little difference to the MLP model in predicting a vector output that represents different output variables (as in the previous example) or a vector output that represents multiple time steps of one variable.

Nevertheless, there are subtle and important differences in the way the training data is prepared. In this section, we will demonstrate the case of developing a multi-step forecast model using a vector model.

Before we look at the specifics of the model, let’s first look at the preparation of data for multi-step forecasting.

As with one-step forecasting, a time series used for multi-step time series forecasting must be split into samples with input and output components.

Both the input and output components will be comprised of multiple time steps and may or may not have the same number of steps.

For example, given the univariate time series:

[10, 20, 30, 40, 50, 60, 70, 80, 90]

We could use the last three time steps as input and forecast the next two time steps.

The first sample would look as follows:

Input:

[10, 20, 30]

Output:

[40, 50]

The *split_sequence()* function below implements this behavior and will split a given univariate time series into samples with a specified number of input and output time steps.

# split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multi-step data preparation from numpy import array # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example splits the univariate series into input and output time steps and prints the input and output components of each.

[10 20 30] [40 50] [20 30 40] [50 60] [30 40 50] [60 70] [40 50 60] [70 80] [50 60 70] [80 90]

Now that we know how to prepare data for multi-step forecasting, let’s look at an MLP model that can learn this mapping.

The MLP can output a vector directly that can be interpreted as a multi-step forecast.

This approach was seen in the previous section were one time step of each output time series was forecasted as a vector.

With the number of input and output steps specified in the *n_steps_in* and *n_steps_out* variables, we can define a multi-step time-series forecasting model.

# define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_steps_in)) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse')

The model can make a prediction for a single sample. We can predict the next two steps beyond the end of the dataset by providing the input:

[70, 80, 90]

We would expect the predicted output to be:

[100, 110]

As expected by the model, the shape of the single sample of input data when making the prediction must be [1, 3] for the 1 sample and 3 time steps (features) of the input and the single feature.

# demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in)) yhat = model.predict(x_input, verbose=0)

Tying all of this together, the MLP for multi-step forecasting with a univariate time series is listed below.

# univariate multi-step vector-output mlp example from numpy import array from keras.models import Sequential from keras.layers import Dense # split a univariate sequence into samples def split_sequence(sequence, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequence)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the sequence if out_end_ix > len(sequence): break # gather input and output parts of the pattern seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # split into samples X, y = split_sequence(raw_seq, n_steps_in, n_steps_out) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_steps_in)) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([70, 80, 90]) x_input = x_input.reshape((1, n_steps_in)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example forecasts and prints the next two time steps in the sequence.

[[102.572365 113.88405 ]]

In the previous sections, we have looked at univariate, multivariate, and multi-step time series forecasting.

It is possible to mix and match the different types of MLP models presented so far for the different problems. This too applies to time series forecasting problems that involve multivariate and multi-step forecasting, but it may be a little more challenging, particularly in preparing the data and defining the shape of inputs and outputs for the model.

In this section, we will look at short examples of data preparation and modeling for multivariate multi-step time series forecasting as a template to ease this challenge, specifically:

- Multiple Input Multi-Step Output.
- Multiple Parallel Input and Multi-Step Output.

Perhaps the biggest stumbling block is in the preparation of data, so this is where we will focus our attention.

There are those multivariate time series forecasting problems where the output series is separate but dependent upon the input time series, and multiple time steps are required for the output series.

For example, consider our multivariate time series from a prior section:

We may use three prior time steps of each of the two input time series to predict two time steps of the output time series.

Input:

10, 15 20, 25 30, 35

Output:

65 85

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this on our contrived dataset. The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training data.

We can see that the shape of the input portion of the samples is three-dimensional, comprised of six samples, with three time steps and two variables for the two input time series.

The output portion of the samples is two-dimensional for the six samples and the two time steps for each sample to be predicted.

The prepared samples are then printed to confirm that the data was prepared as we specified.

(6, 3, 2) (6, 2) [[10 15] [20 25] [30 35]] [65 85] [[20 25] [30 35] [40 45]] [ 85 105] [[30 35] [40 45] [50 55]] [105 125] [[40 45] [50 55] [60 65]] [125 145] [[50 55] [60 65] [70 75]] [145 165] [[60 65] [70 75] [80 85]] [165 185]

We can now develop an MLP model for multi-step predictions using a vector output.

The complete example is listed below.

# multivariate multi-step mlp example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out-1 # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1:out_end_ix, -1] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(n_steps_out)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[70, 75], [80, 85], [90, 95]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the next two time steps of the output sequence beyond the dataset.

We would expect the next two steps to be [185, 205].

It is a challenging framing of the problem with very little data, and the arbitrarily configured version of the model gets close.

[[186.53822 208.41725]]

A problem with parallel time series may require the prediction of multiple time steps of each time series.

For example, consider our multivariate time series from a prior section:

We may use the last three time steps from each of the three time series as input to the model and predict the next time steps of each of the three time series as output.

The first sample in the training dataset would be the following.

Input:

10, 15, 25 20, 25, 45 30, 35, 65

Output:

40, 45, 85 50, 55, 105

The *split_sequences()* function below implements this behavior.

# split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y)

We can demonstrate this function on the small contrived dataset.

The complete example is listed below.

# multivariate multi-step data preparation from numpy import array from numpy import hstack # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) print(X.shape, y.shape) # summarize the data for i in range(len(X)): print(X[i], y[i])

Running the example first prints the shape of the prepared training dataset.

We can see that both the input (*X*) and output (*Y*) elements of the dataset are three dimensional for the number of samples, time steps, and variables or parallel time series respectively.

The input and output elements of each series are then printed side by side so that we can confirm that the data was prepared as we expected.

(5, 3, 3) (5, 2, 3) [[10 15 25] [20 25 45] [30 35 65]] [[ 40 45 85] [ 50 55 105]] [[20 25 45] [30 35 65] [40 45 85]] [[ 50 55 105] [ 60 65 125]] [[ 30 35 65] [ 40 45 85] [ 50 55 105]] [[ 60 65 125] [ 70 75 145]] [[ 40 45 85] [ 50 55 105] [ 60 65 125]] [[ 70 75 145] [ 80 85 165]] [[ 50 55 105] [ 60 65 125] [ 70 75 145]] [[ 80 85 165] [ 90 95 185]]

We can now develop an MLP model to make multivariate multi-step forecasts.

In addition to flattening the shape of the input data, as we have in prior examples, we must also flatten the three-dimensional structure of the output data. This is because the MLP model is only capable of taking vector inputs and outputs.

# flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) # flatten output n_output = y.shape[1] * y.shape[2] y = y.reshape((y.shape[0], n_output))

The complete example is listed below.

# multivariate multi-step mlp example from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense # split a multivariate sequence into samples def split_sequences(sequences, n_steps_in, n_steps_out): X, y = list(), list() for i in range(len(sequences)): # find the end of this pattern end_ix = i + n_steps_in out_end_ix = end_ix + n_steps_out # check if we are beyond the dataset if out_end_ix > len(sequences): break # gather input and output parts of the pattern seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix:out_end_ix, :] X.append(seq_x) y.append(seq_y) return array(X), array(y) # define input sequence in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # convert to [rows, columns] structure in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2, out_seq)) # choose a number of time steps n_steps_in, n_steps_out = 3, 2 # convert into input/output X, y = split_sequences(dataset, n_steps_in, n_steps_out) # flatten input n_input = X.shape[1] * X.shape[2] X = X.reshape((X.shape[0], n_input)) # flatten output n_output = y.shape[1] * y.shape[2] y = y.reshape((y.shape[0], n_output)) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(n_output)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=2000, verbose=0) # demonstrate prediction x_input = array([[60, 65, 125], [70, 75, 145], [80, 85, 165]]) x_input = x_input.reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example fits the model and predicts the values for each of the three time steps for the next two time steps beyond the end of the dataset.

We would expect the values for these series and time steps to be as follows:

90, 95, 185 100, 105, 205

We can see that the model forecast gets reasonably close to the expected values.

[[ 91.28376 96.567 188.37575 100.54482 107.9219 208.108 ]

In this tutorial, you discovered how to develop a suite of Multilayer Perceptron, or MLP, models for a range of standard time series forecasting problems.

Specifically, you learned:

- How to develop MLP models for univariate time series forecasting.
- How to develop MLP models for multivariate time series forecasting.
- How to develop MLP models for multi-step time series forecasting.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Develop Multilayer Perceptron Models for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Use the TimeseriesGenerator for Time Series Forecasting in Keras appeared first on Machine Learning Mastery.

]]>This can be challenging if you have to perform this transformation manually. The Keras deep learning library provides the TimeseriesGenerator to automatically transform both univariate and multivariate time series data into samples, ready to train deep learning models.

In this tutorial, you will discover how to use the Keras TimeseriesGenerator for preparing time series data for modeling with deep learning methods.

After completing this tutorial, you will know:

- How to define the TimeseriesGenerator generator and use it to fit deep learning models.
- How to prepare a generator for univariate time series and fit MLP and LSTM models.
- How to prepare a generator for multivariate time series and fit an LSTM model.

Let’s get started.

This tutorial is divided into six parts; they are:

- Problem with Time Series for Supervised Learning
- How to Use the TimeseriesGenerator
- Univariate Time Series Example
- Multivariate Time Series Example
- Multivariate Inputs and Dependent Series Example
- Multi-step Forecasts Example

**Note**: This tutorial assumes that you are using **Keras v2.2.4** or higher.

Time series data requires preparation before it can be used to train a supervised learning model, such as a deep learning model.

For example, a univariate time series is represented as a vector of observations:

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

A supervised learning algorithm requires that data is provided as a collection of samples, where each sample has an input component (*X*) and an output component (*y*).

X, y example input, example output example input, example output example input, example output ...

The model will learn how to map inputs to outputs from the provided examples.

y = f(X)

A time series must be transformed into samples with input and output components. The transform both informs what the model will learn and how you intend to use the model in the future when making predictions, e.g. what is required to make a prediction (*X*) and what prediction is made (*y*).

For a univariate time series interested in one-step predictions, the observations at prior time steps, so-called lag observations, are used as input and the output is the observation at the current time step.

For example, the above 10-step univariate series can be expressed as a supervised learning problem with three time steps for input and one step as output, as follows:

X, y [1, 2, 3], [4] [2, 3, 4], [5] [3, 4, 5], [6] ...

You can write code to perform this transform yourself; for example, see the post:

Alternately, when you are interested in training neural network models with Keras, you can use the TimeseriesGenerator class.

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

Keras provides the TimeseriesGenerator that can be used to automatically transform a univariate or multivariate time series dataset into a supervised learning problem.

There are two parts to using the TimeseriesGenerator: defining it and using it to train models.

You can create an instance of the class and specify the input and output aspects of your time series problem and it will provide an instance of a Sequence class that can then be used to iterate across the inputs and outputs of the series.

In most time series prediction problems, the input and output series will be the same series.

For example:

# load data inputs = ... outputs = ... # define generator generator = TimeseriesGenerator(inputs, outputs, ...) # iterator generator for i in range(len(generator)): ...

Technically, the class is not a generator in the sense that it is not a Python Generator and you cannot use the *next()* function on it.

In addition to specifying the input and output aspects of your time series problem, there are some additional parameters that you should configure; for example:

**length**: The number of lag observations to use in the input portion of each sample (e.g. 3).**batch_size**: The number of samples to return on each iteration (e.g. 32).

You must define a length argument based on your designed framing of the problem. That is the desired number of lag observations to use as input.

You must also define the batch size as the batch size of your model during training. If the number of samples in your dataset is less than your batch size, you can set the batch size in the generator and in your model to the total number of samples in your generator found via calculating its length; for example:

print(len(generator))

There are also other arguments such as defining start and end offsets into your data, the sampling rate, stride, and more. You are less likely to use these features, but you can see the full API for more details.

The samples are not shuffled by default. This is useful for some recurrent neural networks like LSTMs that maintain state across samples within a batch.

It can benefit other neural networks, such as CNNs and MLPs, to shuffle the samples when training. Shuffling can be enabled by setting the ‘*shuffle*‘ argument to True. This will have the effect of shuffling samples returned for each batch.

At the time of writing, the TimeseriesGenerator is limited to one-step outputs. Multi-step time series forecasting is not supported.

Once a TimeseriesGenerator instance has been defined, it can be used to train a neural network model.

A model can be trained using the TimeseriesGenerator as a data generator. This can be achieved by fitting the defined model using the *fit_generator()* function.

This function takes the generator as an argument. It also takes a *steps_per_epoch* argument that defines the number of samples to use in each epoch. This can be set to the length of the TimeseriesGenerator instance to use all samples in the generator.

For example:

# define generator generator = TimeseriesGenerator(...) # define model model = ... # fit model model.fit_generator(generator, steps_per_epoch=len(generator), ...)

Similarly, the generator can be used to evaluate a fit model by calling the *evaluate_generator()* function, and using a fit model to make predictions on new data with the *predict_generator()* function.

A model fit with the data generator does not have to use the generator versions of the evaluate and predict functions. They can be used only if you wish to have the data generator prepare your data for the model.

We can make the TimeseriesGenerator concrete with a worked example with a small contrived univariate time series dataset.

First, let’s define our dataset.

# define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

We will choose to frame the problem where the last two lag observations will be used to predict the next value in the sequence. For example:

X, y [1, 2] 3

For now, we will use a batch size of 1, so that we can explore the data in the generator.

# define generator n_input = 2 generator = TimeseriesGenerator(series, series, length=n_input, batch_size=1)

Next, we can see how many samples will be prepared by the data generator for this time series.

# number of samples print('Samples: %d' % len(generator))

Finally, we can print the input and output components of each sample, to confirm that the data was prepared as we expected.

for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

The complete example is listed below.

# univariate one step problem from numpy import array from keras.preprocessing.sequence import TimeseriesGenerator # define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # define generator n_input = 2 generator = TimeseriesGenerator(series, series, length=n_input, batch_size=1) # number of samples print('Samples: %d' % len(generator)) # print each sample for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

Running the example first prints the total number of samples in the generator, which is eight.

We can then see that each input array has the shape [1, 2] and each output has the shape [1,].

The observations are prepared as we expected, with two lag observations that will be used as input and the subsequent value in the sequence as the output.

Samples: 8 [[1. 2.]] => [3.] [[2. 3.]] => [4.] [[3. 4.]] => [5.] [[4. 5.]] => [6.] [[5. 6.]] => [7.] [[6. 7.]] => [8.] [[7. 8.]] => [9.] [[8. 9.]] => [10.]

Now we can fit a model on this data and learn to map the input sequence to the output sequence.

We will start with a simple Multilayer Perceptron, or MLP, model.

The generator will be defined so that all samples will be used in each batch, given the small number of samples.

# define generator n_input = 2 generator = TimeseriesGenerator(series, series, length=n_input, batch_size=8)

We can define a simple model with one hidden layer with 50 nodes and an output layer that will make the prediction.

# define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')

We can then fit the model with the generator using the *fit_generator()* function. We only have one batch worth of data in the generator so we’ll set the *steps_per_epoch* to 1. The model will be fit for 200 epochs.

# fit model model.fit_generator(generator, steps_per_epoch=1, epochs=200, verbose=0)

Once fit, we will make an out of sample prediction.

Given the inputs [9, 10], we will make a prediction and expect the model to predict [11], or close to it. The model is not tuned; this is just an example of how to use the generator.

# make a one step prediction out of sample x_input = array([9, 10]).reshape((1, n_input)) yhat = model.predict(x_input, verbose=0)

The complete example is listed below.

# univariate one step problem with mlp from numpy import array from keras.models import Sequential from keras.layers import Dense from keras.preprocessing.sequence import TimeseriesGenerator # define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # define generator n_input = 2 generator = TimeseriesGenerator(series, series, length=n_input, batch_size=8) # define model model = Sequential() model.add(Dense(100, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit_generator(generator, steps_per_epoch=1, epochs=200, verbose=0) # make a one step prediction out of sample x_input = array([9, 10]).reshape((1, n_input)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the generator, fits the model, and makes the out of sample prediction, correctly predicting a value close to 11.

[[11.510406]]

We can also use the generator to fit a recurrent neural network, such as a Long Short-Term Memory network, or LSTM.

The LSTM expects data input to have the shape [*samples, timesteps, features*], whereas the generator described so far is providing lag observations as features or the shape [*samples, features*].

We can reshape the univariate time series prior to preparing the generator from [10, ] to [10, 1] for 10 time steps and 1 feature; for example:

# reshape to [10, 1] n_features = 1 series = series.reshape((len(series), n_features))

The TimeseriesGenerator will then split the series into samples with the shape [*batch, n_input, 1*] or [8, 2, 1] for all eight samples in the generator and the two lag observations used as time steps.

The complete example is listed below.

# univariate one step problem with lstm from numpy import array from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.preprocessing.sequence import TimeseriesGenerator # define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # reshape to [10, 1] n_features = 1 series = series.reshape((len(series), n_features)) # define generator n_input = 2 generator = TimeseriesGenerator(series, series, length=n_input, batch_size=8) # define model model = Sequential() model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit_generator(generator, steps_per_epoch=1, epochs=500, verbose=0) # make a one step prediction out of sample x_input = array([9, 10]).reshape((1, n_input, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Again, running the example prepares the data, fits the model, and predicts the next out of sample value in the sequence.

[[11.092189]]

The TimeseriesGenerator also supports multivariate time series problems.

These are problems where you have multiple parallel series, with observations at the same time step in each series.

We can demonstrate this with an example.

First, we can contrive a dataset of two parallel series.

# define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105])

It is a standard structure to have multivariate time series formatted such that each time series is a separate column and rows are the observations at each time step.

The series we have defined are vectors, but we can convert them into columns. We can reshape each series into an array with the shape [10, 1] for the 10 time steps and 1 feature.

# reshape series in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1))

We can now horizontally stack the columns into a dataset by calling the *hstack()* NumPy function.

# horizontally stack columns dataset = hstack((in_seq1, in_seq2))

We can now provide this dataset to the TimeseriesGenerator directly. We will use the prior two observations of each series as input and the next observation of each series as output.

# define generator n_input = 2 generator = TimeseriesGenerator(dataset, dataset, length=n_input, batch_size=1)

Each sample will then be a three-dimensional array of [1, 2, 2] for the 1 sample, 2 time steps, and 2 features or parallel series. The output will be a two-dimensional series of [1, 2] for the 1 sample and 2 features. The first sample will be:

X, y [[10, 15], [20, 25]] [[30, 35]]

The complete example is listed below.

# multivariate one step problem from numpy import array from numpy import hstack from keras.preprocessing.sequence import TimeseriesGenerator # define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105]) # reshape series in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2)) print(dataset) # define generator n_input = 2 generator = TimeseriesGenerator(dataset, dataset, length=n_input, batch_size=1) # number of samples print('Samples: %d' % len(generator)) # print each sample for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

Running the example will first print the prepared dataset, followed by the total number of samples in the dataset.

Next, the input and output portion of each sample is printed, confirming our intended structure.

[[ 10 15] [ 20 25] [ 30 35] [ 40 45] [ 50 55] [ 60 65] [ 70 75] [ 80 85] [ 90 95] [100 105]] Samples: 8 [[[10. 15.] [20. 25.]]] => [[30. 35.]] [[[20. 25.] [30. 35.]]] => [[40. 45.]] [[[30. 35.] [40. 45.]]] => [[50. 55.]] [[[40. 45.] [50. 55.]]] => [[60. 65.]] [[[50. 55.] [60. 65.]]] => [[70. 75.]] [[[60. 65.] [70. 75.]]] => [[80. 85.]] [[[70. 75.] [80. 85.]]] => [[90. 95.]] [[[80. 85.] [90. 95.]]] => [[100. 105.]]

The three-dimensional structure of the samples means that the generator cannot be used directly for simple models like MLPs.

This could be achieved by first flattening the time series dataset to a one-dimensional vector prior to providing it to the TimeseriesGenerator and set length to the number of steps to use as input multiplied by the number of columns in the series (*n_steps * n_features*).

A limitation of this approach is that the generator will only allow you to predict one variable. You almost certainly may be better off writing your own function to prepare multivariate time series for an MLP than using the TimeseriesGenerator.

The three-dimensional structure of the samples can be used directly by CNN and LSTM models. A complete example for multivariate time series forecasting with the TimeseriesGenerator is listed below.

# multivariate one step problem with lstm from numpy import array from numpy import hstack from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.preprocessing.sequence import TimeseriesGenerator # define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105]) # reshape series in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2)) # define generator n_features = dataset.shape[1] n_input = 2 generator = TimeseriesGenerator(dataset, dataset, length=n_input, batch_size=8) # define model model = Sequential() model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features))) model.add(Dense(2)) model.compile(optimizer='adam', loss='mse') # fit model model.fit_generator(generator, steps_per_epoch=1, epochs=500, verbose=0) # make a one step prediction out of sample x_input = array([[90, 95], [100, 105]]).reshape((1, n_input, n_features)) yhat = model.predict(x_input, verbose=0) print(yhat)

Running the example prepares the data, fits the model, and makes a prediction for the next value in each of the input time series, which we expect to be [110, 115].

[[111.03207 116.58153]]

There are multivariate time series problems where there are one or more input series and a separate output series to be forecasted that is dependent upon the input series.

To make this concrete, we can contrive one example with two input time series and an output series that is the sum of the input series.

# define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105]) out_seq = array([25, 45, 65, 85, 105, 125, 145, 165, 185, 205])

Where values in the output sequence are the sum of values at the same time step in the input time series.

10 + 15 = 25

This is different from prior examples where, given inputs, we wish to predict a value in the target time series for the next time step, not the same time step as the input.

For example, we want samples like:

X, y [10, 15], 25 [20, 25], 45 [30, 35], 65 ...

We don’t want samples like the following:

X, y [10, 15], 45 [20, 25], 65 [30, 35], 85 ...

Nevertheless, the TimeseriesGenerator class assumes that we are predicting the next time step and will provide data as in the second case above.

For example:

# multivariate one step problem from numpy import array from numpy import hstack from keras.preprocessing.sequence import TimeseriesGenerator # define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105]) out_seq = array([25, 45, 65, 85, 105, 125, 145, 165, 185, 205]) # reshape series in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2)) # define generator n_input = 1 generator = TimeseriesGenerator(dataset, out_seq, length=n_input, batch_size=1) # print each sample for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

Running the example prints the input and output portions of the samples with the output values for the next time step rather than the current time step as we may desire for this type of problem.

[[[10. 15.]]] => [[45.]] [[[20. 25.]]] => [[65.]] [[[30. 35.]]] => [[85.]] [[[40. 45.]]] => [[105.]] [[[50. 55.]]] => [[125.]] [[[60. 65.]]] => [[145.]] [[[70. 75.]]] => [[165.]] [[[80. 85.]]] => [[185.]] [[[90. 95.]]] => [[205.]]

We can therefore modify the target series (*out_seq*) and insert an additional value at the beginning in order to push all observations down by one time step.

This artificial shift will allow the preferred framing of the problem.

# shift the target sample by one step out_seq = insert(out_seq, 0, 0)

The complete example with this shift is provided below.

# multivariate one step problem from numpy import array from numpy import hstack from numpy import insert from keras.preprocessing.sequence import TimeseriesGenerator # define dataset in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105]) out_seq = array([25, 45, 65, 85, 105, 125, 145, 165, 185, 205]) # reshape series in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_seq = out_seq.reshape((len(out_seq), 1)) # horizontally stack columns dataset = hstack((in_seq1, in_seq2)) # shift the target sample by one step out_seq = insert(out_seq, 0, 0) # define generator n_input = 1 generator = TimeseriesGenerator(dataset, out_seq, length=n_input, batch_size=1) # print each sample for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

Running the example shows the preferred framing of the problem.

This approach will work regardless of the length of the input sample.

[[[10. 15.]]] => [25.] [[[20. 25.]]] => [45.] [[[30. 35.]]] => [65.] [[[40. 45.]]] => [85.] [[[50. 55.]]] => [105.] [[[60. 65.]]] => [125.] [[[70. 75.]]] => [145.] [[[80. 85.]]] => [165.] [[[90. 95.]]] => [185.]

A benefit of neural network models over many other types of classical and machine learning models is that they can make multi-step forecasts.

That is, that the model can learn to map an input pattern of one or more features to an output pattern of more than one feature. This can be used in time series forecasting to directly forecast multiple future time steps.

This can be achieved either by directly outputting a vector from the model, by specifying the desired number of outputs as the number of nodes in the output layer, or it can be achieved by specialized sequence prediction models such as an encoder-decoder model.

A limitation of the TimeseriesGenerator is that it does not directly support multi-step outputs. Specifically, it will not create the multiple steps that may be required in the target sequence.

Nevertheless, if you prepare your target sequence to have multiple steps, it will honor and use them as the output portion of each sample. This means the onus is on you to prepare the expected output for each time step.

We can demonstrate this with a simple univariate time series with two time steps in the output sequence.

You can see that you must have the same number of rows in the target sequence as you do in the input sequence. In this case, we must know values beyond the values in the input sequence, or trim the input sequence to the length of the target sequence.

# define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) target = array([[1,2],[2,3],[3,4],[4,5],[5,6],[6,7],[7,8],[8,9],[9,10],[10,11]])

The complete example is listed below.

# univariate multi-step problem from numpy import array from keras.preprocessing.sequence import TimeseriesGenerator # define dataset series = array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) target = array([[1,2],[2,3],[3,4],[4,5],[5,6],[6,7],[7,8],[8,9],[9,10],[10,11]]) # define generator n_input = 2 generator = TimeseriesGenerator(series, target, length=n_input, batch_size=1) # print each sample for i in range(len(generator)): x, y = generator[i] print('%s => %s' % (x, y))

Running the example prints the input and output portions of the samples showing the two lag observations as input and the two steps as output in the multi-step forecasting problem.

[[1. 2.]] => [[3. 4.]] [[2. 3.]] => [[4. 5.]] [[3. 4.]] => [[5. 6.]] [[4. 5.]] => [[6. 7.]] [[5. 6.]] => [[7. 8.]] [[6. 7.]] => [[8. 9.]] [[7. 8.]] => [[ 9. 10.]] [[8. 9.]] => [[10. 11.]]

This section provides more resources on the topic if you are looking to go deeper.

- How to Convert a Time Series to a Supervised Learning Problem in Python
- TimeseriesGenerator Keras API
- Sequence Keras API
- Sequential Model Keras API
- Python Generator

In this tutorial, you discovered how to use the Keras TimeseriesGenerator for preparing time series data for modeling with deep learning methods.

Specifically, you learned:

- How to define the TimeseriesGenerator generator and use it to fit deep learning models.
- How to prepare a generator for univariate time series and fit MLP and LSTM models.
- How to prepare a generator for multivariate time series and fit an LSTM model.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Use the TimeseriesGenerator for Time Series Forecasting in Keras appeared first on Machine Learning Mastery.

]]>The post LSTM Model Architecture for Rare Event Time Series Forecasting appeared first on Machine Learning Mastery.

]]>This is surprising as neural networks are known to be able to learn complex non-linear relationships and the LSTM is perhaps the most successful type of recurrent neural network that is capable of directly supporting multivariate sequence prediction problems.

A recent study performed at Uber AI Labs demonstrates how both the automatic feature learning capabilities of LSTMs and their ability to handle input sequences can be harnessed in an end-to-end model that can be used for drive demand forecasting for rare events like public holidays.

In this post, you will discover an approach to developing a scalable end-to-end LSTM model for time series forecasting.

After reading this post, you will know:

- The challenge of multivariate, multi-step forecasting across multiple sites, in this case cities.
- An LSTM model architecture for time series forecasting comprised of separate autoencoder and forecasting sub-models.
- The skill of the proposed LSTM architecture at rare event demand forecasting and the ability to reuse the trained model on unrelated forecasting problems.

Let’s get started.

In this post, we will review the 2017 paper titled “Time-series Extreme Event Forecasting with Neural Networks at Uber” by Nikolay Laptev, et al. presented at the Time Series Workshop, ICML 2017.

This post is divided into four sections; they are:

- Motivation
- Datasets
- Model
- Findings

The goal of the work was to develop an end-to-end forecast model for multi-step time series forecasting that can handle multivariate inputs (e.g. multiple input time series).

The intent of the model was to forecast driver demand at Uber for ride sharing, specifically to forecast demand on challenging days such as holidays where the uncertainty for classical models was high.

Generally, this type of demand forecasting for holidays belongs to an area of study called extreme event prediction.

Extreme event prediction has become a popular topic for estimating peak electricity demand, traffic jam severity and surge pricing for ride sharing and other applications. In fact there is a branch of statistics known as extreme value theory (EVT) that deals directly with this challenge.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

Two existing approaches were described:

**Classical Forecasting Methods**: Where a model was developed per time series, perhaps fit as needed.**Two-Step Approach**: Where classical models were used in conjunction with machine learning models.

The difficulty of these existing models motivated the desire for a single end-to-end model.

Further, a model was required that could generalize across locales, specifically across data collected for each city. This means a model trained on some or all cities with data available and used to make forecasts across some or all cities.

We can summarize this as the general need for a model that supports multivariate inputs, makes multi-step forecasts, and generalizes across multiple sites, in this case cities.

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

The model was fit in a propitiatory Uber dataset comprised of five years of anonymized ride sharing data across top cities in the US.

A five year daily history of completed trips across top US cities in terms of population was used to provide forecasts across all major US holidays.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

The input to each forecast consisted of both the information about each ride, as well as weather, city, and holiday variables.

To circumvent the lack of data we use additional features including weather information (e.g., precipitation, wind speed, temperature) and city level information (e.g., current trips, current users, local holidays).

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

The figure below taken from the paper provides a sample of six variables for one year.

A training dataset was created by splitting the historical data into sliding windows of input and output variables.

The specific size of the look-back and forecast horizon used in the experiments were not specified in the paper.

Time series data was scaled by normalizing observations per batch of samples and each input series was de-trended, but not deseasonalized.

Neural networks are sensitive to unscaled data, therefore we normalize every minibatch. Furthermore, we found that de-trending the data, as opposed to de-seasoning, produces better results.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

LSTMs, e.g. Vanilla LSTMs, were evaluated on the problem and show relatively poor performance.

This is not surprising as it mirrors findings elsewhere.

Our initial LSTM implementation did not show superior performance relative to the state of the art approach.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

A more elaborate architecture was used, comprised of two LSTM models:

**Feature Extractor**: Model for distilling an input sequence down to a feature vector that may be used as input for making a forecast.**Forecaster**: Model that uses the extracted features and other inputs to make a forecast.

An LSTM autoencoder model was developed for use as the feature extraction model and a Stacked LSTM was used as the forecast model.

We found that the vanilla LSTM model’s performance is worse than our baseline. Thus, we propose a new architecture, that leverages an autoencoder for feature extraction, achieving superior performance compared to our baseline.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

When making a forecast, time series data is first provided to the autoencoders, which is compressed to multiple feature vectors that are averaged and concatenated. The feature vectors are then provided as input to the forecast model in order to make a prediction.

… the model first primes the network by auto feature extraction, which is critical to capture complex time-series dynamics during special events at scale. […] Features vectors are then aggregated via an ensemble technique (e.g., averaging or other methods). The final vector is then concatenated with the new input and fed to LSTM forecaster for prediction.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

It is not clear what exactly is provided to the autoencoder when making a prediction, although we may guess that it is a multivariate time series for the city being forecasted with observations prior to the interval being forecasted.

A multivariate time series as input to the autoencoder will result in multiple encoded vectors (one for each series) that could be concatenated. It is not clear what role averaging may take at this point, although we may guess that it is an averaging of multiple models performing the autoencoding process.

The authors comment that it would be possible to make the autoencoder a part of the forecast model, and that this was evaluated, but the separate model resulted in better performance.

Having a separate auto-encoder module, however, produced better results in our experience.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

More details of the developed model were made available in the slides used when presenting the paper.

The input for the autoencoder was 512 LSTM units and the bottleneck in the autoencoder used to create the encoded feature vectors as 32 or 64 LSTM units.

The encoded feature vectors are provided to the forecast model with ‘*new input*‘, although it is not specified what this new input is; we could guess that it is a time series, perhaps a multivariate time series of the city being forecasted with observations prior to the forecast interval. Or, features extracted from this series as the blog post on the paper suggests (although I’m skeptical as the paper and slides contradict this).

The model was trained on a lot of data, which is a general requirement of stacked LSTMs or perhaps LSTMs in general.

The described production Neural Network Model was trained on thousands of time-series with thousands of data points each.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

The model is not retrained when making new forecasts.

An interesting approach to estimating forecast uncertainty was also implemented that used the bootstrap.

It involved estimating model uncertainty and forecast uncertainty separately, using the autoencoder and the forecast model respectively. Inputs were provided to a given model and dropout of the activations (as commented in the slides) was used. This process was repeated 100 times, and the model and forecast error terms were used in an estimate of the forecast uncertainty.

This approach to forecast uncertainty may be better described in the 2017 paper “Deep and Confident Prediction for Time Series at Uber.”

The model was evaluated with a special focus on demand forecasting for U.S. holidays by U.S. city.

The specifics of the model evaluation were not specified.

The new generalized LSTM forecast model was found to outperform the existing model used at Uber, which may be impressive if we assume that the existing model was well tuned.

The results presented show a 2%-18% forecast accuracy improvement compared to the current proprietary method comprising a univariate timeseries and machine learned model.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

The model trained on the Uber dataset was then applied directly to a subset of the M3-Competition dataset comprised of about 1,500 monthly univariate time series forecasting datasets.

This is a type of transfer learning, a highly-desirable goal that allows the reuse of deep learning models across problem domains.

Surprisingly, the model performed well, not great compared to the top performing methods, but better than many sophisticated models. The result is suggests that perhaps with fine tuning (e.g. as is done in other transfer learning case studies) the model could be reused and be skillful.

Importantly, the authors suggest that perhaps the most beneficial application of deep LSTM models to time series forecasting are situations where:

- There are a large number of time series.
- There are a large number of observations for each series.
- There is a strong correlation between time series.

From our experience there are three criteria for picking a neural network model for time-series: (a) number of timeseries (b) length of time-series and (c) correlation among the time-series. If (a), (b) and (c) are high then the neural network might be the right choice, otherwise classical timeseries approach may work best.

— Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.

This is summarized well by a slide used in the presentation of the paper.

This section provides more resources on the topic if you are looking to go deeper.

- Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017.
- Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks, 2017.
- Time-Series Modeling with Neural Networks at Uber, Slides, 2017.
- Time-series Extreme Event Forecasting Case study, Slides 2018.
- Time Series Workshop, ICML 2017
- Deep and Confident Prediction for Time Series at Uber, 2017.

In this post, you discovered a scalable end-to-end LSTM model for time series forecasting.

Specifically, you learned:

- The challenge of multivariate, multi-step forecasting across multiple sites, in this case cities.
- An LSTM model architecture for time series forecasting comprised of separate autoencoder and forecasting sub-models.
- The skill of the proposed LSTM architecture at rare event demand forecasting and the ability to reuse the trained model on unrelated forecasting problems.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post LSTM Model Architecture for Rare Event Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post Comparing Classical and Machine Learning Algorithms for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>An important recent study evaluated and compared the performance of many classical and modern machine learning and deep learning methods on a large and diverse set of more than 1,000 univariate time series forecasting problems.

The results of this study suggest that simple classical methods, such as linear methods and exponential smoothing, outperform complex and sophisticated methods, such as decision trees, Multilayer Perceptrons (MLP), and Long Short-Term Memory (LSTM) network models.

These findings highlight the requirement to both evaluate classical methods and use their results as a baseline when evaluating any machine learning and deep learning methods for time series forecasting in order demonstrate that their added complexity is adding skill to the forecast.

In this post, you will discover the important findings of this recent study evaluating and comparing the performance of a classical and modern machine learning methods on a large and diverse set of time series forecasting datasets.

After reading this post, you will know:

- Classical methods like ETS and ARIMA out-perform machine learning and deep learning methods for one-step forecasting on univariate datasets.
- Classical methods like Theta and ARIMA out-perform machine learning and deep learning methods for multi-step forecasting on univariate datasets.
- Machine learning and deep learning methods do not yet deliver on their promise for univariate time series forecasting, and there is much work to do.

Let’s get started.

Spyros Makridakis, et al. published a study in 2018 titled “Statistical and Machine Learning forecasting methods: Concerns and ways forward.”

In this post, we will take a close look at the study by Makridakis, et al. that carefully evaluated and compared classical time series forecasting methods to the performance of modern machine learning methods.

This post is divided into seven sections; they are:

- Study Motivation
- Time Series Datasets
- Time Series Forecasting Methods
- Data Preparation
- One-Step Forecasting Results
- Multi-Step Forecasting Results
- Outcomes

The goal of the study was to clearly demonstrate the capability of a suite of different machine learning methods as compared to classical time series forecasting methods on a very large and diverse collection of univariate time series forecasting problems.

The study was a response to the increasing number of papers and claims that machine learning and deep learning methods offer superior results for time series forecasting with little objective evidence.

Literally hundreds of papers propose new ML algorithms, suggesting methodological advances and accuracy improvements. Yet, limited objective evidence is available regarding their relative performance as a standard forecasting tool.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

The authors clearly lay out three issues with the flood of claims; they are:

- Their conclusions are based on a few, or even a single time series, raising questions about the statistical significance of the results and their generalization.
- The methods are evaluated for short-term forecasting horizons, often one-step-ahead, not considering medium and long-term ones.
- No benchmarks are used to compare the accuracy of ML methods versus alternative ones.

As a response, the study includes eight classical methods and 10 machine learning methods evaluated using one-step and multiple-step forecasts across a collection of 1,045 monthly time series.

Although not definitive, the results are intended to be objective and robust.

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The time series datasets used in the study were drawn from the time series datasets used in the M3-Competition.

The M3-Competition was the third in a series of competitions that sought to discover exactly what algorithms perform well in practice on real time series forecasting problems. The results of the competition were published in the 2000 paper titled “The M3-Competition: Results, Conclusions and Implications.”

The datasets used in the competition were drawn from a wide range of industries and had a range of different time intervals, from hourly to annual.

The 3003 series of the M3-Competition were selected on a quota basis to include various types of time series data (micro, industry, macro, etc.) and different time intervals between successive observations (yearly, quarterly, etc.).

The table below, taken from the paper, provides a summary of the 3,003 datasets used in the competition.

The finding of the competition was that simpler time series forecasting methods outperform more sophisticated methods, including neural network models.

This study, the previous two M-Competitions and many other empirical studies have proven, beyond the slightest doubt, that elaborate theoretical constructs or more sophisticated methods do not necessarily improve post-sample forecasting accuracy, over simple methods, although they can better fit a statistical model to the available historical data.

— The M3-Competition: Results, Conclusions and Implications, 2000.

The more recent study that we are reviewing in this post that evaluate machine learning methods selected a subset of 1,045 time series with a monthly interval from those used in the M3 competition.

… evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

The study evaluates the performance of eight classical (or simpler) methods and 10 machine learning methods.

… of eight traditional statistical methods and eight popular ML ones, […], plus two more that have become popular during recent years.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

The eight classical methods evaluated were as follows:

- Naive 2, which is actually a random walk model adjusted for season.
- Simple Exponential Smoothing.
- Holt.
- Damped exponential smoothing.
- Average of SES, Holt, and Damped.
- Theta method.
- ARIMA, automatic.
- ETS, automatic.

A total of eight machine learning methods were used in an effort to reproduce and compare to results presented in the 2010 paper “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.”

They were:

- Multi-Layer Perceptron (MLP)
- Bayesian Neural Network (BNN)
- Radial Basis Functions (RBF)
- Generalized Regression Neural Networks (GRNN), also called kernel regression
- K-Nearest Neighbor regression (KNN)
- CART regression trees (CART)
- Support Vector Regression (SVR)
- Gaussian Processes (GP)

An additional two ‘*modern*‘ neural network algorithms were also added to the list given the recent rise in their adoption; they were:

- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM)

A careful data preparation methodology was used, again, based on the methodology described in the 2010 paper “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.”

In that paper, each time series was adjusted using a power transform, deseasonalized and detrended.

[…] before computing the 18 forecasts, they preprocessed the series in order to achieve stationarity in their mean and variance. This was done using the log transformation, then deseasonalization and finally scaling, while first differences were also considered for removing the component of trend.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

Inspired by these operations, variations of five different data transforms were applied for an MLP for one-step forecasting and their results were compared. The five transforms were:

- Original data.
- Box-Cox Power Transform.
- Deseasonalizing the data.
- Detrending the data.
- All three transforms (power, deseasonalize, detrend).

Generally, it was found that the best approach was to apply a power transform and deseasonalize the data, and perhaps detrend the series as well.

The best combination according to sMAPE is number 7 (Box-Cox transformation, deseasonalization) while the best one according to MASE is number 10 (Box-Cox transformation, deseasonalization and detrending)

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

All models were evaluated using one-step time series forecasting.

Specifically, the last 18 time steps were used as a test set, and models were fit on all remaining observations. A separate one-step forecast was made for each of the 18 observations in the test set, presumably using a walk-forward validation method where true observations were used as input in order to make each forecast.

The forecasting model was developed using the first n – 18 observations, where n is the length of the series. Then, 18 forecasts were produced and their accuracy was evaluated compared to the actual values not used in developing the forecasting model.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

Reviewing the results, the MLP and BNN were found to achieve the best performance from all of the machine learning methods.

The results […] show that MLP and BNN outperform the remaining ML methods.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

A surprising result was that RNNs and LSTMs were found to perform poorly.

It should be noted that RNN is among the less accurate ML methods, demonstrating that research progress does not necessarily guarantee improvements in forecasting performance. This conclusion also applies in the performance of LSTM, another popular and more advanced ML method, which does not enhance forecasting accuracy too.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

Comparing the performance of all methods, it was found that the machine learning methods were all out-performed by simple classical methods, where ETS and ARIMA models performed the best overall.

This finding confirms the results from previous similar studies and competitions.

Multi-step forecasting involves predicting multiple steps ahead of the last known observation.

Three approaches to multi-step forecasting were evaluated for the machine learning methods; they were:

- Iterative forecasting
- Direct forecasting
- Multi-neural network forecasting

The classical methods were found to outperform the machine learning methods again.

In this case, methods such as Theta, ARIMA, and a combination of exponential smoothing (Comb) were found to achieve the best performance.

In brief, statistical models seem to generally outperform ML methods across all forecasting horizons, with Theta, Comb and ARIMA being the dominant ones among the competitors according to both error metrics examined.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

The study provides important supporting evidence that classical methods may dominate univariate time series forecasting, at least on the types of forecasting problems evaluated.

The study demonstrates the worse performance and the increase in computational cost of machine learning and deep learning methods for univariate time series forecasting for both one-step and multi-step forecasts.

These findings strongly encourage the use of classical methods, such as ETS, ARIMA, and others as a first step before more elaborate methods are explored, and requires that the results from these simpler methods be used as a baseline in performance that more elaborate methods must clear in order to justify their usage.

It also highlights the need to not just consider the careful use of data preparation methods, but to actively test multiple different combinations of data preparation schemes for a given problem in order to discover what works best, even in the case of classical methods.

Machine learning and deep learning methods may still achieve better performance on specific univariate time series problems and should be evaluated.

The study does not look at more complex time series problems, such as those datasets with:

- Complex irregular temporal structures.
- Missing observations
- Heavy noise.
- Complex interrelationships between multiple variates.

The study concludes with an honest puzzlement at why machine learning methods perform so poorly in practice, given their impressive performance in other areas of artificial intelligence.

The most interesting question and greatest challenge is to find the reasons for their poor performance with the objective of improving their accuracy and exploiting their huge potential. AI learning algorithms have revolutionized a wide range of applications in diverse fields and there is no reason that the same cannot be achieved with the ML methods in forecasting. Thus, we must find how to be applied to improve their ability to forecast more accurately.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

Comments are made by the authors regarding LSTMs and RNNs, that are generally believed to be the deep learning approach for sequence prediction problems in general, and in this case their clearly poor performance in practice.

[…] one would expect RNN and LSTM, which are more advanced types of NNs, to be far more accurate than the ARIMA and the rest of the statistical methods utilized.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

They comment that LSTMs appear to be more suited at fitting or overfitting the training dataset rather than forecasting it.

Another interesting example could be the case of LSTM that compared to simpler NNs like RNN and MLP, report better model fitting but worse forecasting accuracy

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

There is work to do and machine learning methods and deep learning methods hold the promise of better learning time series data than classical statistical methods, and even doing so directly on the raw observations via automatic feature learning.

Given their ability to learn, ML methods should do better than simple benchmarks, like exponential smoothing. Accepting the problem is the first step in devising workable solutions and we hope that those in the field of AI and ML will accept the empirical findings and work to improve the forecasting accuracy of their methods.

— Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.

This section provides more resources on the topic if you are looking to go deeper.

- Makridakis Competitions, Wikipedia
- The M3-Competition: Results, Conclusions and Implications, 2000.
- The M4 Competition: Results, findings, conclusion and way forward, 2018.
- Statistical and Machine Learning forecasting methods: Concerns and ways forward, 2018.
- An Empirical Comparison of Machine Learning Models for Time Series Forecasting, 2010.

In this post, you discovered the important findings of a recent study evaluating and comparing the performance of classical and modern machine learning methods on a large and diverse set of time series forecasting datasets.

Specifically, you learned:

- Classical methods like ETS and ARIMA out-perform machine learning and deep learning methods for one-step forecasting on univariate datasets.
- Classical methods like Theta and ARIMA out-perform machine learning and deep learning methods for multi-step forecasting on univariate datasets.
- Machine learning and deep learning methods do not yet deliver on their promise for univariate time series forecasting and there is much work to do.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post Comparing Classical and Machine Learning Algorithms for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Develop Deep Learning Models for Univariate Time Series Forecasting appeared first on Machine Learning Mastery.

]]>This feature of neural networks can be used for time series forecasting problems, where models can be developed directly on the raw observations without the direct need to scale the data using normalization and standardization or to make the data stationary by differencing.

Impressively, simple deep learning neural network models are capable of making skillful forecasts as compared to naive models and tuned SARIMA models on univariate time series forecasting problems that have both trend and seasonal components with no pre-processing.

In this tutorial, you will discover how to develop a suite of deep learning models for univariate time series forecasting.

After completing this tutorial, you will know:

- How to develop a robust test harness using walk-forward validation for evaluating the performance of neural network models.
- How to develop and evaluate simple multilayer Perceptron and convolutional neural networks for time series forecasting.
- How to develop and evaluate LSTMs, CNN-LSTMs, and ConvLSTM neural network models for time series forecasting.

Let’s get started.

**Updated Apr/2019**: Updated the link to dataset.

This tutorial is divided into five parts; they are:

- Problem Description
- Model Evaluation Test Harness
- Multilayer Perceptron Model
- Convolutional Neural Network Model
- Recurrent Neural Network Models

The ‘*monthly car sales*‘ dataset summarizes the monthly car sales in Quebec, Canada between 1960 and 1968.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-car-sales.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

# load series = read_csv('monthly-car-sales.csv', header=0, index_col=0)

Once loaded, we can summarize the shape of the dataset in order to determine the number of observations.

# summarize shape print(series.shape)

We can then create a line plot of the series to get an idea of the structure of the series.

# plot pyplot.plot(series) pyplot.show()

We can tie all of this together; the complete example is listed below.

# load and plot dataset from pandas import read_csv from matplotlib import pyplot # load series = read_csv('monthly-car-sales.csv', header=0, index_col=0) # summarize shape print(series.shape) # plot pyplot.plot(series) pyplot.show()

Running the example first prints the shape of the dataset.

(108, 1)

The dataset is monthly and has nine years, or 108 observations. In our testing, will use the last year, or 12 observations, as the test set.

A line plot is created. The dataset has an obvious trend and seasonal component. The period of the seasonal component could be six months or 12 months.

From prior experiments, we know that a naive model can achieve a root mean squared error, or RMSE, of 1841.155 by taking the median of the observations at the three prior years for the month being predicted; for example:

yhat = median(-12, -24, -36)

Where the negative indexes refer to observations in the series relative to the end of the historical data for the month being predicted.

From prior experiments, we know that a SARIMA model can achieve an RMSE of 1551.842 with the configuration of SARIMA(0, 0, 0),(1, 1, 0),12 where no elements are specified for the trend and a seasonal difference with a period of 12 is calculated and an AR model of one season is used.

The performance of the naive model provides a lower bound on a model that is considered skillful. Any model that achieves a predictive performance of lower than 1841.155 on the last 12 months has skill.

The performance of the SARIMA model provides a measure of a good model on the problem. Any model that achieves a predictive performance lower than 1551.842 on the last 12 months should be adopted over a SARIMA model.

Now that we have defined our problem and expectations of model skill, we can look at defining the test harness.

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In this section, we will develop a test harness for developing and evaluating different types of neural network models for univariate time series forecasting.

This section is divided into the following parts:

- Train-Test Split
- Series as Supervised Learning
- Walk-Forward Validation
- Repeat Evaluation
- Summarize Performance
- Worked Example

The first step is to split the loaded series into train and test sets.

We will use the first eight years (96 observations) for training and the last 12 for the test set.

The *train_test_split()* function below will split the series taking the raw observations and the number of observations to use in the test set as arguments.

# split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:]

Next, we need to be able to frame the univariate series of observations as a supervised learning problem so that we can train neural network models.

A supervised learning framing of a series means that the data needs to be split into multiple examples that the model learn from and generalize across.

Each sample must have both an input component and an output component.

The input component will be some number of prior observations, such as three years or 36 time steps.

The output component will be the total sales in the next month because we are interested in developing a model to make one-step forecasts.

We can implement this using the shift() function on the pandas DataFrame. It allows us to shift a column down (forward in time) or back (backward in time). We can take the series as a column of data, then create multiple copies of the column, shifted forward or backward in time in order to create the samples with the input and output elements we require.

When a series is shifted down, *NaN* values are introduced because we don’t have values beyond the start of the series.

For example, the series defined as a column:

(t) 1 2 3 4

Can be shifted and inserted as a column beforehand:

(t-1), (t) Nan, 1 1, 2 2, 3 3, 4 4 NaN

We can see that on the second row, the value 1 is provided as input as an observation at the prior time step, and 2 is the next value in the series that can be predicted, or learned by the model to be predicted when 1 is presented as input.

Rows with *NaN* values can be removed.

The *series_to_supervised()* function below implements this behavior, allowing you to specify the number of lag observations to use in the input and the number to use in the output for each sample. It will also remove rows that have *NaN* values as they cannot be used to train or test a model.

# transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values

Time series forecasting models can be evaluated on a test set using walk-forward validation.

Walk-forward validation is an approach where the model makes a forecast for each observation in the test dataset one at a time. After each forecast is made for a time step in the test dataset, the true observation for the forecast is added to the test dataset and made available to the model.

Simpler models can be refit with the observation prior to making the subsequent prediction. More complex models, such as neural networks, are not refit given the much greater computational cost.

Nevertheless, the true observation for the time step can then be used as part of the input for making the prediction on the next time step.

First, the dataset is split into train and test sets. We will call the *train_test_split()* function to perform this split and pass in the pre-specified number of observations to use as the test data.

A model will be fit once on the training dataset for a given configuration.

We will define a generic *model_fit()* function to perform this operation that can be filled in for the given type of neural network that we may be interested in later. The function takes the training dataset and the model configuration and returns the fit model ready for making predictions.

# fit a model def model_fit(train, config): return None

Each time step of the test dataset is enumerated. A prediction is made using the fit model.

Again, we will define a generic function named *model_predict()* that takes the fit model, the history, and the model configuration and makes a single one-step prediction.

# forecast with a pre-fit model def model_predict(model, history, config): return 0.0

The prediction is added to a list of predictions and the true observation from the test set is added to a list of observations that was seeded with all observations from the training dataset. This list is built up during each step in the walk-forward validation, allowing the model to make a one-step prediction using the most recent history.

All of the predictions can then be compared to the true values in the test set and an error measure calculated.

We will calculate the root mean squared error, or RMSE, between predictions and the true values.

RMSE is calculated as the square root of the average of the squared differences between the forecasts and the actual values. The *measure_rmse()* implements this below using the mean_squared_error() scikit-learn function to first calculate the mean squared error, or MSE, before calculating the square root.

# root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted))

The complete *walk_forward_validation()* function that ties all of this together is listed below.

It takes the dataset, the number of observations to use as the test set, and the configuration for the model, and returns the RMSE for the model performance on the test set.

# walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error

Neural network models are stochastic.

This means that, given the same model configuration and the same training dataset, a different internal set of weights will result each time the model is trained that will in turn have a different performance.

This is a benefit, allowing the model to be adaptive and find high performing configurations to complex problems.

It is also a problem when evaluating the performance of a model and in choosing a final model to use to make predictions.

To address model evaluation, we will evaluate a model configuration multiple times via walk-forward validation and report the error as the average error across each evaluation.

This is not always possible for large neural networks and may only make sense for small networks that can be fit in minutes or hours.

The *repeat_evaluate()* function below implements this and allows the number of repeats to be specified as an optional parameter that defaults to 30 and returns a list of model performance scores: in this case, RMSE values.

# repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores

Finally, we need to summarize the performance of a model from the multiple repeats.

We will summarize the performance first using summary statistics, specifically the mean and the standard deviation.

We will also plot the distribution of model performance scores using a box and whisker plot to help get an idea of the spread of performance.

The *summarize_scores()* function below implements this, taking the name of the model that was evaluated and the list of scores from each repeated evaluation, printing the summary and showing a plot.

# summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show()

Now that we have defined the elements of the test harness, we can tie them all together and define a simple persistence model.

Specifically, we will calculate the median of a subset of prior observations relative to the time to be forecasted.

We do not need to fit a model so the *model_fit()* function will be implemented to simply return *None*.

# fit a model def model_fit(train, config): return None

We will use the config to define a list of index offsets in the prior observations relative to the time to be forecasted that will be used as the prediction. For example, 12 will use the observation 12 months ago (-12) relative to the time to be forecasted.

# define config config = [12, 24, 36]

The model_predict() function can be implemented to use this configuration to collect the observations, then return the median of those observations.

# forecast with a pre-fit model def model_predict(model, history, config): values = list() for offset in config: values.append(history[-offset]) return median(values)

The complete example of using the framework with a simple persistence model is listed below.

# persistence from math import sqrt from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, interval): return [data[i] - data[i - interval] for i in range(interval, len(data))] # fit a model def model_fit(train, config): return None # forecast with a pre-fit model def model_predict(model, history, config): values = list() for offset in config: values.append(history[-offset]) return median(values) # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [12, 24, 36] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('persistence', scores)

Running the example prints the RMSE of the model evaluated using walk-forward validation on the final 12 months of data.

The model is evaluated 30 times, although, because the model has no stochastic element, the score is the same each time.

> 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 > 1841.156 persistence: 1841.156 RMSE (+/- 0.000)

We can see that the RMSE of the model is 1841, providing a lower-bound of performance by which we can evaluate whether a model is skillful or not on the problem.

Now that we have a robust test harness, we can use it to evaluate a suite of neural network models.

The first network that we will evaluate is a multilayer Perceptron, or MLP for short.

This is a simple feed-forward neural network model that should be evaluated before more elaborate models are considered.

MLPs can be used for time series forecasting by taking multiple observations at prior time steps, called lag observations, and using them as input features and predicting one or more time steps from those observations.

This is exactly the framing of the problem provided by the *series_to_supervised()* function in the previous section.

The training dataset is therefore a list of samples, where each sample has some number of observations from months prior to the time being forecasted, and the forecast is the next month in the sequence. For example:

X, y month1, month2, month3, month4 month2, month3, month4, month5 month3, month4, month5, month6 ...

The model will attempt to generalize over these samples, such that when a new sample is provided beyond what is known by the model, it can predict something useful; for example:

X, y month4, month5, month6, ???

We will implement a simple MLP using the Keras deep learning library.

The model will have an input layer with some number of prior observations. This can be specified using the *input_dim* argument when we define the first hidden layer. The model will have a single hidden layer with some number of nodes, then a single output layer.

We will use the rectified linear activation function on the hidden layer as it performs well. We will use a linear activation function (the default) on the output layer because we are predicting a continuous value.

The loss function for the network will be the mean squared error loss, or MSE, and we will use the efficient Adam flavor of stochastic gradient descent to train the network.

# define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam')

The model will be fit for some number of training epochs (exposures to the training data) and batch size can be specified to define how often the weights are updated within each epoch.

The *model_fit()* function for fitting an MLP model on the training dataset is listed below.

The function expects the config to be a list with the following configuration hyperparameters:

**n_input**: The number of lag observations to use as input to the model.**n_nodes**: The number of nodes to use in the hidden layer.**n_epochs**: The number of times to expose the model to the whole training dataset.**n_batch**: The number of samples within an epoch after which the weights are updated.

# fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch = config # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] # define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Making a prediction with a fit MLP model is as straightforward as calling the *predict()* function and passing in one sample worth of input values required to make the prediction.

yhat = model.predict(x_input, verbose=0)

In order to make a prediction beyond the limit of known data, this requires that the last n known observations are taken as an array and used as input.

The *predict()* function expects one or more samples of inputs when making a prediction, so providing a single sample requires the array to have the shape [*1, n_input*], where *n_input* is the number of time steps that the model expects as input.

Similarly, the *predict()* function returns an array of predictions, one for each sample provided as input. In the case of one prediction, there will be an array with one value.

The *model_predict()* function below implements this behavior, taking the model, the prior observations, and model configuration as arguments, formulating an input sample and making a one-step prediction that is then returned.

# forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _ = config # prepare data x_input = array(history[-n_input:]).reshape(1, n_input) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0]

We now have everything we need to evaluate an MLP model on the monthly car sales dataset.

A simple grid search of model hyperparameters was performed and the configuration below was chosen. This may not be an optimal configuration, but is the best that was found.

**n_input**: 24 (e.g. 24 months)**n_nodes**: 500**n_epochs**: 100**n_batch**: 100

This configuration can be defined as a list:

# define config config = [24, 500, 100, 100]

Note that when the training data is framed as a supervised learning problem, there are only 72 samples that can be used to train the model.

Using a batch size of 72 or more means that the model is being trained using batch gradient descent instead of mini-batch gradient descent. This is often used for small datasets and means that weight updates and gradient calculations are performed at the end of each epoch, instead of multiple times within each epoch.

The complete code example is listed below.

# evaluate mlp from math import sqrt from numpy import array from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch = config # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] # define model model = Sequential() model.add(Dense(n_nodes, activation='relu', input_dim=n_input)) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _ = config # prepare data x_input = array(history[-n_input:]).reshape(1, n_input) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [24, 500, 100, 100] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('mlp', scores)

Running the example prints the RMSE for each of the 30 repeated evaluations of the model.

At the end of the run, the average and standard deviation RMSE are reported of about 1,526 sales.

We can see that, on average, the chosen configuration has better performance than both the naive model (1841.155) and the SARIMA model (1551.842).

This is impressive given that the model operated on the raw data directly without scaling or the data being made stationary.

> 1629.203 > 1642.219 > 1472.483 > 1662.055 > 1452.480 > 1465.535 > 1116.253 > 1682.667 > 1642.626 > 1700.183 > 1444.481 > 1673.217 > 1602.342 > 1655.895 > 1319.387 > 1591.972 > 1592.574 > 1361.607 > 1450.348 > 1314.529 > 1549.505 > 1569.750 > 1427.897 > 1478.926 > 1474.990 > 1458.993 > 1643.383 > 1457.925 > 1558.934 > 1708.278 mlp: 1526.688 RMSE (+/- 134.789)

A box and whisker plot of the RMSE scores is created to summarize the spread of the performance for the model.

This helps to understand the spread of the scores. We can see that although on average the performance of the model is impressive, the spread is large. The standard deviation is a little more than 134 sales, meaning a worse case model run that is 2 or 3 standard deviations in error from the mean error may be worse than the naive model.

A challenge in using the MLP model is in harnessing the higher skill and minimizing the variance of the model across multiple runs.

This problem applies generally for neural networks. There are many strategies that you could use, but perhaps the simplest is simply to train multiple final models on all of the available data and use them in an ensemble when making predictions, e.g. the prediction is the average of 10-to-30 models.

Convolutional Neural Networks, or CNNs, are a type of neural network developed for two-dimensional image data, although they can be used for one-dimensional data such as sequences of text and time series.

When operating on one-dimensional data, the CNN reads across a sequence of lag observations and learns to extract features that are relevant for making a prediction.

We will define a CNN with two convolutional layers for extracting features from the input sequences. Each will have a configurable number of filters and kernel size and will use the rectified linear activation function. The number of filters determines the number of parallel fields on which the weighted inputs are read and projected. The kernel size defines the number of time steps read within each snapshot as the network reads along the input sequence.

model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, 1))) model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu'))

A max pooling layer is used after convolutional layers to distill the weighted input features into those that are most salient, reducing the input size by 1/4. The pooled inputs are flattened to one long vector before being interpreted and used to make a one-step prediction.

model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1))

The CNN model expects input data to be in the form of multiple samples, where each sample has multiple input time steps, the same as the MLP in the previous section.

One difference is that the CNN can support multiple features or types of observations at each time step, which are interpreted as channels of an image. We only have a single feature at each time step, therefore the required three-dimensional shape of the input data will be [*n_samples, n_input, 1*].

train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1))

The *model_fit()* function for fitting the CNN model on the training dataset is listed below.

The model takes the following five configuration parameters as a list:

**n_input**: The number of lag observations to use as input to the model.**n_filters**: The number of parallel filters.**n_kernel**: The number of time steps considered in each read of the input sequence.**n_epochs**: The number of times to expose the model to the whole training dataset.**n_batch**: The number of samples within an epoch after which the weights are updated.

# fit a model def model_fit(train, config): # unpack config n_input, n_filters, n_kernel, n_epochs, n_batch = config # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1)) # define model model = Sequential() model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, 1))) model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Making a prediction with the fit CNN model is very much like making a prediction with the fit MLP model in the previous section.

The one difference is in the requirement that we specify the number of features observed at each time step, which in this case is 1. Therefore, when making a single one-step prediction, the shape of the input array must be:

[1, n_input, 1]

The *model_predict()* function below implements this behavior.

# forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, _ = config # prepare data x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0]

A simple grid search of model hyperparameters was performed and the configuration below was chosen. This is not an optimal configuration, but is the best that was found.

The chosen configuration is as follows:

**n_input**: 36 (e.g. 3 years or 3 * 12)**n_filters**: 256**n_kernel**: 3**n_epochs**: 100**n_batch**: 100 (e.g. batch gradient descent)

This can be specified as a list as follows:

# define config config = [36, 256, 3, 100, 100]

Tying all of this together, the complete example is listed below.

# evaluate cnn from math import sqrt from numpy import array from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # fit a model def model_fit(train, config): # unpack config n_input, n_filters, n_kernel, n_epochs, n_batch = config # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1)) # define model model = Sequential() model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(n_input, 1))) model.add(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, _ = config # prepare data x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [36, 256, 3, 100, 100] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('cnn', scores)

Running the example first prints the RMSE for each repeated evaluation of the model.

At the end of the run, we can see that indeed the model is skillful, achieving an average RMSE of 1,524.067, which is better than the naive model, the SARIMA model, and even the MLP model in the previous section.

This is impressive given that the model operated on the raw data directly without scaling or the data being made stationary.

The standard deviation of the score is large, at about 57 sales, but is 1/3 the size of the variance observed with the MLP model in the previous section. We have some confidence that in a bad-case scenario (3 standard deviations), the model RMSE will remain below (better than) the performance of the naive model.

> 1551.031 > 1495.743 > 1449.408 > 1526.017 > 1466.118 > 1566.535 > 1649.204 > 1455.782 > 1574.214 > 1541.790 > 1489.140 > 1506.035 > 1513.197 > 1530.714 > 1511.328 > 1471.518 > 1555.596 > 1552.026 > 1531.727 > 1472.978 > 1620.242 > 1424.153 > 1456.393 > 1581.114 > 1539.286 > 1489.795 > 1652.620 > 1537.349 > 1443.777 > 1567.179 cnn: 1524.067 RMSE (+/- 57.148)

A box and whisker plot of the scores is created to help understand the spread of error across the runs.

We can see that the spread does seem to be biased towards larger error values, as we would expect, although the upper whisker of the plot (in this case, the largest error that are not outliers) is still limited at an RMSE of 1,650 sales.

Recurrent neural networks, or RNNs, are those types of neural networks that use an output of the network from a prior step as an input in attempt to automatically learn across sequence data.

The Long Short-Term Memory, or LSTM, network is a type of RNN whose implementation addresses the general difficulties in training RNNs on sequence data that results in a stable model. It achieves this by learning the weights for internal gates that control the recurrent connections within each node.

Although developed for sequence data, LSTMs have not proven effective on time series forecasting problems where the output is a function of recent observations, e.g. an autoregressive type forecasting problem, such as the car sales dataset.

Nevertheless, we can develop LSTM models for autoregressive problems and use them as a point of comparison with other neural network models.

In this section, we will explore three variations on the LSTM model for univariate time series forecasting; they are:

**LSTM**: The LSTM network as-is.**CNN-LSTM**: A CNN network that learns input features and an LSTM that interprets them.**ConvLSTM**: A combination of CNNs and LSTMs where the LSTM units read input data using the convolutional process of a CNN.

The LSTM neural network can be used for univariate time series forecasting.

As an RNN, it will read each time step of an input sequence one step at a time. The LSTM has an internal memory allowing it to accumulate internal state as it reads across the steps of a given input sequence.

At the end of the sequence, each node in a layer of hidden LSTM units will output a single value. This vector of values summarizes what the LSTM learned or extracted from the input sequence. This can be interpreted by a fully connected layer before a final prediction is made.

# define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, 1))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam')

Like the CNN, the LSTM can support multiple variables or features at each time step. As the car sales dataset only has one value at each time step, we can fix this at 1, both when defining the input to the network in the input_shape argument [*n_input, 1*], and in defining the shape of the input samples.

train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1))

Unlike the MLP and CNN that do not read the sequence data one-step at a time, the LSTM does perform better if the data is stationary. This means that difference operations are performed to remove the trend and seasonal structure.

In the case of the car sales dataset, we can make the data stationery by performing a seasonal adjustment, that is subtracting the value from one year ago from each observation.

adjusted = value - value[-12]

This can be performed systematically for the entire training dataset. It also means that the first year of observations must be discarded as we have no prior year of data to difference them with.

The *difference()* function below will difference a provided dataset with a provided offset, called the difference order, e.g. 12 for one year of months prior.

# difference dataset def difference(data, interval): return [data[i] - data[i - interval] for i in range(interval, len(data))]

We can make the difference order a hyperparameter to the model and only perform the operation if a value other than zero is provided.

The *model_fit()* function for fitting an LSTM model is provided below.

The model expects a list of five model hyperparameters; they are:

**n_input**: The number of lag observations to use as input to the model.**n_nodes**: The number of LSTM units to use in the hidden layer.**n_epochs**: The number of times to expose the model to the whole training dataset.**n_batch**: The number of samples within an epoch after which the weights are updated.**n_diff**: The difference order or 0 if not used.

# fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1)) # define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, 1))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Making a prediction with the LSTM model is the same as making a prediction with a CNN model.

A single input must have the three-dimensional structure of samples, timesteps, and features, which in this case we only have 1 sample and 1 feature: [*1, n_input, 1*].

If the difference operation was performed, we must add back the value that was subtracted after the model has made a forecast. We must also difference the historical data prior to formulating the single input used to make a prediction.

The *model_predict()* function below implements this behavior.

# forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0]

A simple grid search of model hyperparameters was performed and the configuration below was chosen. This is not an optimal configuration, but is the best that was found.

The chosen configuration is as follows:

**n_input**: 36 (i.e. 3 years or 3 * 12)**n_nodes**: 50**n_epochs**: 100**n_batch**: 100 (i.e. batch gradient descent)**n_diff**: 12 (i.e. seasonal difference)

This can be specified as a list:

# define config config = [36, 50, 100, 100, 12]

Tying all of this together, the complete example is listed below.

# evaluate lstm from math import sqrt from numpy import array from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, interval): return [data[i] - data[i - interval] for i in range(interval, len(data))] # fit a model def model_fit(train, config): # unpack config n_input, n_nodes, n_epochs, n_batch, n_diff = config # prepare data if n_diff > 0: train = difference(train, n_diff) data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], 1)) # define model model = Sequential() model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, 1))) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_input, _, _, _, n_diff = config # prepare data correction = 0.0 if n_diff > 0: correction = history[-n_diff] history = difference(history, n_diff) x_input = array(history[-n_input:]).reshape((1, n_input, 1)) # forecast yhat = model.predict(x_input, verbose=0) return correction + yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [36, 50, 100, 100, 12] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('lstm', scores)

Running the example, we can see the RMSE for each repeated evaluation of the model.

At the end of the run, we can see that the average RMSE is about 2,109, which is worse than the naive model. This suggests that the chosen model is not skillful, and it was the best that could be found given the same resources used to find model configurations in the previous sections.

This provides further evidence (although weak evidence) that LSTMs, at least alone, are perhaps a bad fit for autoregressive-type sequence prediction problems.

> 2129.480 > 2169.109 > 2078.290 > 2257.222 > 2014.911 > 2197.283 > 2028.176 > 2110.718 > 2100.388 > 2157.271 > 1940.103 > 2086.588 > 1986.696 > 2168.784 > 2188.813 > 2086.759 > 2128.095 > 2126.467 > 2077.463 > 2057.679 > 2209.818 > 2067.082 > 1983.346 > 2157.749 > 2145.071 > 2266.130 > 2105.043 > 2128.549 > 1952.002 > 2188.287 lstm: 2109.779 RMSE (+/- 81.373)

A box and whisker plot is also created summarizing the distribution of RMSE scores.

Even the base case for the model did not achieve the performance of a naive model.

We have seen that the CNN model is capable of automatically learning and extracting features from the raw sequence data without scaling or differencing.

We can combine this capability with the LSTM where a CNN model is applied to sub-sequences of input data, the results of which together form a time series of extracted features that can be interpreted by an LSTM model.

This combination of a CNN model used to read multiple subsequences over time by an LSTM is called a CNN-LSTM model.

The model requires that each input sequence, e.g. 36 months, is divided into multiple subsequences, each read by the CNN model, e.g. 3 subsequence of 12 time steps. It may make sense to divide the sub-sequences by years, but this is just a hypothesis, and other splits could be used, such as six subsequences of six time steps. Therefore, this splitting is parameterized with the *n_seq* and *n_steps* for the number of subsequences and number of steps per subsequence parameters.

train_x = train_x.reshape((train_x.shape[0], n_seq, n_steps, 1))

The number of lag observations per sample is simply (*n_seq * n_steps*).

This is a 4-dimensional input array now with the dimensions:

[samples, subsequences, timesteps, features]

The same CNN model must be applied to each input subsequence.

We can achieve this by wrapping the entire CNN model in a *TimeDistributed* layer wrapper.

model = Sequential() model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(None,n_steps,1)))) model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu'))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten()))

The output of one application of the CNN submodel will be a vector. The output of the submodel to each input subsequence will be a time series of interpretations that can be interpreted by an LSTM model. This can be followed by a fully connected layer to interpret the outcomes of the LSTM and finally an output layer for making one-step predictions.

model.add(LSTM(n_nodes, activation='relu')) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1))

The complete *model_fit()* function is listed below.

The model expects a list of seven hyperparameters; they are:

**n_seq**: The number of subsequences within a sample.**n_steps**: The number of time steps within each subsequence.**n_filters**: The number of parallel filters.**n_kernel**: The number of time steps considered in each read of the input sequence.**n_nodes**: The number of LSTM units to use in the hidden layer.**n_epochs**: The number of times to expose the model to the whole training dataset.**n_batch**: The number of samples within an epoch after which the weights are updated.

# fit a model def model_fit(train, config): # unpack config n_seq, n_steps, n_filters, n_kernel, n_nodes, n_epochs, n_batch = config n_input = n_seq * n_steps # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], n_seq, n_steps, 1)) # define model model = Sequential() model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(None,n_steps,1)))) model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu'))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten())) model.add(LSTM(n_nodes, activation='relu')) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

Making a prediction with the fit model is much the same as the LSTM or CNN, although with the addition of splitting each sample into subsequences with a given number of time steps.

# prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, n_steps, 1))

The updated *model_predict()* function is listed below.

# forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_seq, n_steps, _, _, _, _, _ = config n_input = n_seq * n_steps # prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, n_steps, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0]

A simple grid search of model hyperparameters was performed and the configuration below was chosen. This may not be an optimal configuration, but it is the best that was found.

**n_seq**: 3 (i.e. 3 years)**n_steps**: 12 (i.e. 1 year of months)**n_filters**: 64**n_kernel**: 3**n_nodes**: 100**n_epochs**: 200**n_batch**: 100 (i.e. batch gradient descent)

We can define the configuration as a list; for example:

# define config config = [3, 12, 64, 3, 100, 200, 100]

The complete example of evaluating the CNN-LSTM model for forecasting the univariate monthly car sales is listed below.

# evaluate cnn lstm from math import sqrt from numpy import array from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import TimeDistributed from keras.layers import Flatten from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # fit a model def model_fit(train, config): # unpack config n_seq, n_steps, n_filters, n_kernel, n_nodes, n_epochs, n_batch = config n_input = n_seq * n_steps # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], n_seq, n_steps, 1)) # define model model = Sequential() model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu', input_shape=(None,n_steps,1)))) model.add(TimeDistributed(Conv1D(filters=n_filters, kernel_size=n_kernel, activation='relu'))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten())) model.add(LSTM(n_nodes, activation='relu')) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_seq, n_steps, _, _, _, _, _ = config n_input = n_seq * n_steps # prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, n_steps, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [3, 12, 64, 3, 100, 200, 100] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('cnn-lstm', scores)

Running the example prints the RMSE for each repeated evaluation of the model.

The final averaged RMSE is reported at the end of about 1,626, which is lower than the naive model, but still higher than a SARIMA model. The standard deviation of this score is also very large, suggesting that the chosen configuration may not be as stable as the standalone CNN model.

> 1543.533 > 1421.895 > 1467.927 > 1441.125 > 1750.995 > 1321.498 > 1571.657 > 1845.298 > 1621.589 > 1425.065 > 1675.232 > 1807.288 > 2922.295 > 1391.861 > 1626.655 > 1633.177 > 1667.572 > 1577.285 > 1590.235 > 1557.385 > 1784.982 > 1664.839 > 1741.729 > 1437.992 > 1772.076 > 1289.794 > 1685.976 > 1498.123 > 1618.627 > 1448.361 cnn-lstm: 1626.735 RMSE (+/- 279.850)

A box and whisker plot is also created summarizing the distribution of RMSE scores.

The plot shows one single outlier of very poor performance just below 3,000 sales.

It is possible to perform a convolutional operation as part of the read of the input sequence within each LSTM unit.

This means, rather than reading a sequence one step at a time, the LSTM would read a block or subsequence of observations at a time using a convolutional process, like a CNN.

This is different to first reading an extracting features with an LSTM and interpreting the result with an LSTM; this is performing the CNN operation at each time step as part of the LSTM.

This type of model is called a Convolutional LSTM, or ConvLSTM for short. It is provided in Keras as a layer called ConvLSTM2D for 2D data. We can configure it for use with 1D sequence data by assuming that we have one row with multiple columns.

As with the CNN-LSTM, the input data is split into subsequences where each subsequence has a fixed number of time steps, although we must also specify the number of rows in each subsequence, which in this case is fixed at 1.

train_x = train_x.reshape((train_x.shape[0], n_seq, 1, n_steps, 1))

The shape is five-dimensional, with the dimensions:

[samples, subsequences, rows, columns, features]

Like the CNN, the ConvLSTM layer allows us to specify the number of filter maps and the size of the kernel used when reading the input sequences.

model.add(ConvLSTM2D(filters=n_filters, kernel_size=(1,n_kernel), activation='relu', input_shape=(n_seq, 1, n_steps, 1)))

The output of the layer is a sequence of filter maps that must first be flattened before it can be interpreted and followed by an output layer.

The model expects a list of seven hyperparameters, the same as the CNN-LSTM; they are:

**n_seq**: The number of subsequences within a sample.**n_steps**: The number of time steps within each subsequence.**n_filters**: The number of parallel filters.**n_kernel**: The number of time steps considered in each read of the input sequence.**n_nodes**: The number of LSTM units to use in the hidden layer.**n_epochs**: The number of times to expose the model to the whole training dataset.**n_batch**: The number of samples within an epoch after which the weights are updated.

The *model_fit()* function that implements all of this is listed below.

# fit a model def model_fit(train, config): # unpack config n_seq, n_steps, n_filters, n_kernel, n_nodes, n_epochs, n_batch = config n_input = n_seq * n_steps # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], n_seq, 1, n_steps, 1)) # define model model = Sequential() model.add(ConvLSTM2D(filters=n_filters, kernel_size=(1,n_kernel), activation='relu', input_shape=(n_seq, 1, n_steps, 1))) model.add(Flatten()) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model

A prediction is made with the fit model in the same way as the CNN-LSTM, although with the additional rows dimension that we fix to 1.

# prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, 1, n_steps, 1))

The *model_predict()* function for making a single one-step prediction is listed below.

# forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_seq, n_steps, _, _, _, _, _ = config n_input = n_seq * n_steps # prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, 1, n_steps, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0]

A simple grid search of model hyperparameters was performed and the configuration below was chosen.

This may not be an optimal configuration, but is the best that was found.

**n_seq**: 3 (i.e. 3 years)**n_steps**: 12 (i.e. 1 year of months)**n_filters**: 256**n_kernel**: 3**n_nodes**: 200**n_epochs**: 200**n_batch**: 100 (i.e. batch gradient descent)

We can define the configuration as a list; for example:

# define config config = [3, 12, 256, 3, 200, 200, 100]

We can tie all of this together. The complete code listing for the ConvLSTM model evaluated for one-step forecasting of the monthly car sales dataset is listed below.

# evaluate convlstm from math import sqrt from numpy import array from numpy import mean from numpy import std from pandas import DataFrame from pandas import concat from pandas import read_csv from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers import ConvLSTM2D from matplotlib import pyplot # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # transform list into supervised learning format def series_to_supervised(data, n_in=1, n_out=1): df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values agg.dropna(inplace=True) return agg.values # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # difference dataset def difference(data, interval): return [data[i] - data[i - interval] for i in range(interval, len(data))] # fit a model def model_fit(train, config): # unpack config n_seq, n_steps, n_filters, n_kernel, n_nodes, n_epochs, n_batch = config n_input = n_seq * n_steps # prepare data data = series_to_supervised(train, n_in=n_input) train_x, train_y = data[:, :-1], data[:, -1] train_x = train_x.reshape((train_x.shape[0], n_seq, 1, n_steps, 1)) # define model model = Sequential() model.add(ConvLSTM2D(filters=n_filters, kernel_size=(1,n_kernel), activation='relu', input_shape=(n_seq, 1, n_steps, 1))) model.add(Flatten()) model.add(Dense(n_nodes, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') # fit model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0) return model # forecast with a pre-fit model def model_predict(model, history, config): # unpack config n_seq, n_steps, _, _, _, _, _ = config n_input = n_seq * n_steps # prepare data x_input = array(history[-n_input:]).reshape((1, n_seq, 1, n_steps, 1)) # forecast yhat = model.predict(x_input, verbose=0) return yhat[0] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # fit model model = model_fit(train, cfg) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = model_predict(model, history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) print(' > %.3f' % error) return error # repeat evaluation of a config def repeat_evaluate(data, config, n_test, n_repeats=30): # fit and evaluate the model n times scores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)] return scores # summarize model performance def summarize_scores(name, scores): # print a summary scores_m, score_std = mean(scores), std(scores) print('%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std)) # box and whisker plot pyplot.boxplot(scores) pyplot.show() series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values # data split n_test = 12 # define config config = [3, 12, 256, 3, 200, 200, 100] # grid search scores = repeat_evaluate(data, config, n_test) # summarize scores summarize_scores('convlstm', scores)

Running the example prints the RMSE for each repeated evaluation of the model.

The final averaged RMSE is reported at the end of about 1,660, which is lower than the naive model, but still higher than a SARIMA model.

It is a result that is perhaps on par with the CNN-LSTM model. The standard deviation of this score is also very large, suggesting that the chosen configuration may not be as stable as the standalone CNN model.

> 1825.246 > 1862.674 > 1684.313 > 1310.448 > 2109.668 > 1507.912 > 1431.118 > 1442.692 > 1400.548 > 1732.381 > 1523.824 > 1611.898 > 1805.970 > 1616.015 > 1649.466 > 1521.884 > 2025.655 > 1622.886 > 2536.448 > 1526.532 > 1866.631 > 1562.625 > 1491.386 > 1506.270 > 1843.981 > 1653.084 > 1650.430 > 1291.353 > 1558.616 > 1653.231 convlstm: 1660.840 RMSE (+/- 248.826)

A box and whisker plot is also created, summarizing the distribution of RMSE scores.

This section lists some ideas for extending the tutorial that you may wish to explore.

**Data Preparation**. Explore whether data preparation, such as normalization, standardization, and/or differencing can list the performance of any of the models.**Grid Search Hyperparameters**. Implement a grid search of the hyperparameters for one model to see if you can further lift performance.**Learning Curve Diagnostics**. Create a single fit of one model and review the learning curves on train and validation splits of the dataset, then use the diagnostics of the learning curves to further tune the model hyperparameters in order to improve model performance.**History Size**. Explore different amounts of historical data (lag inputs) for one model to see if you can further improve model performance**Reduce Variance of Final Model**. Explore one or more strategies to reduce the variance for one of the neural network models.**Update During Walk-Forward**. Explore whether re-fitting or updating a neural network model as part of walk-forward validation can further improve model performance.**More Parameterization**. Explore adding further model parameterization for one model, such as the use of additional layers.

If you explore any of these extensions, I’d love to know.

This section provides more resources on the topic if you are looking to go deeper.

- pandas.DataFrame.shift API
- sklearn.metrics.mean_squared_error API
- matplotlib.pyplot.boxplot API
- Keras Sequence Model API

In this tutorial, you discovered how to develop a suite of deep learning models for univariate time series forecasting.

Specifically, you learned:

- How to develop a robust test harness using walk-forward validation for evaluating the performance of neural network models.
- How to develop and evaluate simple multilayer Perceptron and convolutional neural networks for time series forecasting.
- How to develop and evaluate LSTMs, CNN-LSTMs, and ConvLSTM neural network models for time series forecasting.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Develop Deep Learning Models for Univariate Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Grid Search Naive Methods for Univariate Time Series Forecasting appeared first on Machine Learning Mastery.

]]>It is important to evaluate the performance of simple forecasting methods on univariate time series forecasting problems before using more sophisticated methods as their performance provides a lower-bound and point of comparison that can be used to determine of a model has skill or not for a given problem.

Although simple, methods such as the naive and average forecast strategies can be tuned to a specific problem in terms of the choice of which prior observation to persist or how many prior observations to average. Often, tuning the hyperparameters of these simple strategies can provide a more robust and defensible lower bound on model performance, as well as surprising results that may inform the choice and configuration of more sophisticated methods.

In this tutorial, you will discover how to develop a framework from scratch for grid searching simple naive and averaging strategies for time series forecasting with univariate data.

After completing this tutorial, you will know:

- How to develop a framework for grid searching simple models from scratch using walk-forward validation.
- How to grid search simple model hyperparameters for daily time series data for births.
- How to grid search simple model hyperparameters for monthly time series data for shampoo sales, car sales, and temperature.

Let’s get started.

**Updated Apr/2019**: Updated the links to the datasets.**Updated Feb/2020**: Fixed typo in the selection of the seasonality in last two cases.

This tutorial is divided into six parts; they are:

- Simple Forecasting Strategies
- Develop a Grid Search Framework
- Case Study 1: No Trend or Seasonality
- Case Study 2: Trend
- Case Study 3: Seasonality
- Case Study 4: Trend and Seasonality

It is important and useful to test simple forecast strategies prior to testing more complex models.

Simple forecast strategies are those that assume little or nothing about the nature of the forecast problem and are fast to implement and calculate.

The results can be used as a baseline in performance and used as a point of a comparison. If a model can perform better than the performance of a simple forecast strategy, then it can be said to be skillful.

There are two main themes to simple forecast strategies; they are:

**Naive**, or using observations values directly.**Average**, or using a statistic calculated on previous observations.

Let’s take a closer look at both of these strategies.

A naive forecast involves using the previous observation directly as the forecast without any change.

It is often called the persistence forecast as the prior observation is persisted.

This simple approach can be adjusted slightly for seasonal data. In this case, the observation at the same time in the previous cycle may be persisted instead.

This can be further generalized to testing each possible offset into the historical data that could be used to persist a value for a forecast.

For example, given the series:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

We could persist the last observation (relative index -1) as the value 9 or persist the second last prior observation (relative index -2) as 8, and so on.

One step above the naive forecast is the strategy of averaging prior values.

All prior observations are collected and averaged, either using the mean or the median, with no other treatment to the data.

In some cases, we may want to shorten the history used in the average calculation to the last few observations.

We can generalize this to the case of testing each possible set of n-prior observations to be included into the average calculation.

For example, given the series:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

We could average the last one observation (9), the last two observations (8, 9), and so on.

In the case of seasonal data, we may want to average the last n-prior observations at the same time in the cycle as the time that is being forecasted.

For example, given the series with a 3-step cycle:

[1, 2, 3, 1, 2, 3, 1, 2, 3]

We could use a window size of 3 and average the last one observation (-3 or 1), the last two observations (-3 or 1, and -(3 * 2) or 1), and so on.

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

In this section, we will develop a framework for grid searching the two simple forecast strategies described in the previous section, namely the naive and average strategies.

We can start off by implementing a naive forecast strategy.

For a given dataset of historical observations, we can persist any value in that history, that is from the previous observation at index -1 to the first observation in the history at -(len(data)).

The *naive_forecast()* function below implements the naive forecast strategy for a given offset from 1 to the length of the dataset.

# one-step naive forecast def naive_forecast(history, n): return history[-n]

We can test this function out on a small contrived dataset.

# one-step naive forecast def naive_forecast(history, n): return history[-n] # define dataset data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] print(data) # test naive forecast for i in range(1, len(data)+1): print(naive_forecast(data, i))

Running the example first prints the contrived dataset, then the naive forecast for each offset in the historical dataset.

[10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0

We can now look at developing a function for the average forecast strategy.

Averaging the last n observations is straight-forward; for example:

from numpy import mean result = mean(history[-n:])

We may also want to test out the median in those cases where the distribution of observations is non-Gaussian.

from numpy import median result = median(history[-n:])

The *average_forecast()* function below implements this taking the historical data and a config array or tuple that specifies the number of prior values to average as an integer, and a string that describe the way to calculate the average (‘*mean*‘ or ‘*median*‘).

# one-step average forecast def average_forecast(history, config): n, avg_type = config # mean of last n values if avg_type is 'mean': return mean(history[-n:]) # median of last n values return median(history[-n:])

The complete example on a small contrived dataset is listed below.

from numpy import mean from numpy import median # one-step average forecast def average_forecast(history, config): n, avg_type = config # mean of last n values if avg_type is 'mean': return mean(history[-n:]) # median of last n values return median(history[-n:]) # define dataset data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] print(data) # test naive forecast for i in range(1, len(data)+1): print(average_forecast(data, (i, 'mean')))

Running the example forecasts the next value in the series as the mean value from contiguous subsets of prior observations from -1 to -10, inclusively.

[10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] 100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0

We can update the function to support averaging over seasonal data, respecting the seasonal offset.

An offset argument can be added to the function that when not set to 1 will determine the number of prior observations backwards to count before collecting values from which to include in the average.

For example, if n=1 and offset=3, then the average is calculated from the single value at n*offset or 1*3 = -3. If n=2 and offset=3, then the average is calculated from the values at 1*3 or -3 and 2*3 or -6.

We can also add some protection to raise an exception when a seasonal configuration (n * offset) extends beyond the end of the historical observations.

The updated function is listed below.

# one-step average forecast def average_forecast(history, config): n, offset, avg_type = config values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # mean of last n values if avg_type is 'mean': return mean(values) # median of last n values return median(values)

We can test out this function on a small contrived dataset with a seasonal cycle.

The complete example is listed below.

from numpy import mean from numpy import median # one-step average forecast def average_forecast(history, config): n, offset, avg_type = config values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # mean of last n values if avg_type is 'mean': return mean(values) # median of last n values return median(values) # define dataset data = [10.0, 20.0, 30.0, 10.0, 20.0, 30.0, 10.0, 20.0, 30.0] print(data) # test naive forecast for i in [1, 2, 3]: print(average_forecast(data, (i, 3, 'mean')))

Running the example calculates the mean values of [10], [10, 10] and [10, 10, 10].

[10.0, 20.0, 30.0, 10.0, 20.0, 30.0, 10.0, 20.0, 30.0] 10.0 10.0 10.0

It is possible to combine both the naive and the average forecast strategies together into the same function.

There is a little overlap between the methods, specifically the *n-*offset into the history that is used to either persist values or determine the number of values to average.

It is helpful to have both strategies supported by one function so that we can test a suite of configurations for both strategies at once as part of a broader grid search of simple models.

The *simple_forecast()* function below combines both strategies into a single function.

# one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values)

Next, we need to build up some functions for fitting and evaluating a model repeatedly via walk-forward validation, including splitting a dataset into train and test sets and evaluating one-step forecasts.

We can split a list or NumPy array of data using a slice given a specified size of the split, e.g. the number of time steps to use from the data in the test set.

The *train_test_split()* function below implements this for a provided dataset and a specified number of time steps to use in the test set.

# split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:]

After forecasts have been made for each step in the test dataset, they need to be compared to the test set in order to calculate an error score.

There are many popular error scores for time series forecasting. In this case, we will use root mean squared error (RMSE), but you can change this to your preferred measure, e.g. MAPE, MAE, etc.

The *measure_rmse()* function below will calculate the RMSE given a list of actual (the test set) and predicted values.

# root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted))

We can now implement the walk-forward validation scheme. This is a standard approach to evaluating a time series forecasting model that respects the temporal ordering of observations.

First, a provided univariate time series dataset is split into train and test sets using the *train_test_split(**)* function. Then the number of observations in the test set are enumerated. For each we fit a model on all of the history and make a one step forecast. The true observation for the time step is then added to the history, and the process is repeated. The *simple_forecast**()* function is called in order to fit a model and make a prediction. Finally, an error score is calculated by comparing all one-step forecasts to the actual test set by calling the *measure_rmse()* function.

The *walk_forward_validation()* function below implements this, taking a univariate time series, a number of time steps to use in the test set, and an array of model configuration.

# walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error

If you are interested in making multi-step predictions, you can change the call to *predict(**)* in the *simple_forecast**()* function and also change the calculation of error in the *measure_rmse()* function.

We can call *walk_forward_validation()* repeatedly with different lists of model configurations.

One possible issue is that some combinations of model configurations may not be called for the model and will throw an exception.

We can trap exceptions and ignore warnings during the grid search by wrapping all calls to *walk_forward_validation(**)* with a try-except and a block to ignore warnings. We can also add debugging support to disable these protections in the case we want to see what is really going on. Finally, if an error does occur, we can return a *None* result; otherwise, we can print some information about the skill of each model evaluated. This is helpful when a large number of models are evaluated.

The *score_model()* function below implements this and returns a tuple of (key and result), where the key is a string version of the tested model configuration.

# score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result)

Next, we need a loop to test a list of different model configurations.

This is the main function that drives the grid search process and will call the *score_model()* function for each model configuration.

We can dramatically speed up the grid search process by evaluating model configurations in parallel. One way to do that is to use the Joblib library.

We can define a Parallel object with the number of cores to use and set it to the number of scores detected in your hardware.

executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing')

We can then create a list of tasks to execute in parallel, which will be one call to the score_model() function for each model configuration we have.

tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list)

Finally, we can use the *Parallel* object to execute the list of tasks in parallel.

scores = executor(tasks)

That’s it.

We can also provide a non-parallel version of evaluating all model configurations in case we want to debug something.

scores = [score_model(data, n_test, cfg) for cfg in cfg_list]

The result of evaluating a list of configurations will be a list of tuples, each with a name that summarizes a specific model configuration and the error of the model evaluated with that configuration as either the RMSE or *None* if there was an error.

We can filter out all scores set to *None*.

scores = [r for r in scores if r[1] != None]

We can then sort all tuples in the list by the score in ascending order (best are first), then return this list of scores for review.

The *grid_search()* function below implements this behavior given a univariate time series dataset, a list of model configurations (list of lists), and the number of time steps to use in the test set. An optional parallel argument allows the evaluation of models across all cores to be tuned on or off, and is on by default.

# grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores

We’re nearly done.

The only thing left to do is to define a list of model configurations to try for a dataset.

We can define this generically. The only parameter we may want to specify is the periodicity of the seasonal component in the series (offset), if one exists. By default, we will assume no seasonal component.

The *simple_configs()* function below will create a list of model configurations to evaluate.

The function only requires the maximum length of the historical data as an argument and optionally the periodicity of any seasonal component, which is defaulted to 1 (no seasonal component).

# create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs

We now have a framework for grid searching simple model hyperparameters via one-step walk-forward validation.

It is generic and will work for any in-memory univariate time series provided as a list or NumPy array.

We can make sure all the pieces work together by testing it on a contrived 10-step dataset.

The complete example is listed below.

# grid search simple forecasts from math import sqrt from numpy import mean from numpy import median from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from sklearn.metrics import mean_squared_error # one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values) # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs if __name__ == '__main__': # define dataset data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] print(data) # data split n_test = 4 # model configs max_length = len(data) - n_test cfg_list = simple_configs(max_length) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example first prints the contrived time series dataset.

Next, the model configurations and their errors are reported as they are evaluated.

Finally, the configurations and the error for the top three configurations are reported.

We can see that the persistence model with a configuration of 1 (e.g. persist the last observation) achieves the best performance of the simple models tested, as would be expected.

[10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] > Model[[1, 1, 'persist']] 10.000 > Model[[2, 1, 'persist']] 20.000 > Model[[2, 1, 'mean']] 15.000 > Model[[2, 1, 'median']] 15.000 > Model[[3, 1, 'persist']] 30.000 > Model[[4, 1, 'persist']] 40.000 > Model[[5, 1, 'persist']] 50.000 > Model[[5, 1, 'mean']] 30.000 > Model[[3, 1, 'mean']] 20.000 > Model[[4, 1, 'median']] 25.000 > Model[[6, 1, 'persist']] 60.000 > Model[[4, 1, 'mean']] 25.000 > Model[[3, 1, 'median']] 20.000 > Model[[6, 1, 'mean']] 35.000 > Model[[5, 1, 'median']] 30.000 > Model[[6, 1, 'median']] 35.000 done [1, 1, 'persist'] 10.0 [2, 1, 'mean'] 15.0 [2, 1, 'median'] 15.0

Now that we have a robust framework for grid searching simple model hyperparameters, let’s test it out on a suite of standard univariate time series datasets.

The results demonstrated on each dataset provide a baseline of performance that can be used to compare more sophisticated methods, such as SARIMA, ETS, and even machine learning methods.

The ‘daily female births’ dataset summarizes the daily total female births in California, USA in 1959.

The dataset has no obvious trend or seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘*daily-total-female-births.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('daily-total-female-births.csv', header=0, index_col=0)

The dataset has one year, or 365 observations. We will use the first 200 for training and the remaining 165 as the test set.

The complete example grid searching the daily female univariate time series forecasting problem is listed below.

# grid search simple forecast for daily female births from math import sqrt from numpy import mean from numpy import median from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values) # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs if __name__ == '__main__': # define dataset series = read_csv('daily-total-female-births.csv', header=0, index_col=0) data = series.values print(data) # data split n_test = 165 # model configs max_length = len(data) - n_test cfg_list = simple_configs(max_length) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example prints the model configurations and the RMSE are printed as the models are evaluated.

The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 6.93 births with the following configuration:

**Strategy**: Average**n**: 22**function**: mean()

This is surprising given the lack of trend or seasonality, I would have expected either a persistence of -1 or an average of the entire historical dataset to result in the best performance.

... > Model[[186, 1, 'mean']] 7.523 > Model[[200, 1, 'median']] 7.681 > Model[[186, 1, 'median']] 7.691 > Model[[187, 1, 'persist']] 11.137 > Model[[187, 1, 'mean']] 7.527 done [22, 1, 'mean'] 6.930411499775709 [23, 1, 'mean'] 6.932293117115201 [21, 1, 'mean'] 6.951918385845375

The ‘shampoo’ dataset summarizes the monthly sales of shampoo over a three-year period.

The dataset contains an obvious trend but no obvious seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘*shampoo.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

# parse dates def custom_parser(x): return datetime.strptime('195'+x, '%Y-%m') # load dataset series = read_csv('shampoo.csv', header=0, index_col=0, date_parser=custom_parser)

The dataset has three years, or 36 observations. We will use the first 24 for training and the remaining 12 as the test set.

The complete example grid searching the shampoo sales univariate time series forecasting problem is listed below.

# grid search simple forecast for monthly shampoo sales from math import sqrt from numpy import mean from numpy import median from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from sklearn.metrics import mean_squared_error from pandas import read_csv from pandas import datetime # one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values) # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs # parse dates def custom_parser(x): return datetime.strptime('195'+x, '%Y-%m') if __name__ == '__main__': # load dataset series = read_csv('shampoo.csv', header=0, index_col=0, date_parser=custom_parser) data = series.values print(data.shape) # data split n_test = 12 # model configs max_length = len(data) - n_test cfg_list = simple_configs(max_length) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example prints the configurations and the RMSE are printed as the models are evaluated.

The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 95.69 sales with the following configuration:

**Strategy**: Persist**n**: 2

This is surprising as the trend structure of the data would suggest that persisting the previous value (-1) would be the best approach, not persisting the second last value.

... > Model[[23, 1, 'mean']] 209.782 > Model[[23, 1, 'median']] 221.863 > Model[[24, 1, 'persist']] 305.635 > Model[[24, 1, 'mean']] 213.466 > Model[[24, 1, 'median']] 226.061 done [2, 1, 'persist'] 95.69454007413378 [2, 1, 'mean'] 96.01140340258198 [2, 1, 'median'] 96.01140340258198

The ‘monthly mean temperatures’ dataset summarizes the monthly average air temperatures in Nottingham Castle, England from 1920 to 1939 in degrees Fahrenheit.

The dataset has an obvious seasonal component and no obvious trend.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-mean-temp.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('monthly-mean-temp.csv', header=0, index_col=0)

The dataset has 20 years, or 240 observations. We will trim the dataset to the last five years of data (60 observations) in order to speed up the model evaluation process and use the last year or 12 observations for the test set.

# trim dataset to 5 years data = data[-(5*12):]

The period of the seasonal component is about one year, or 12 observations. We will use this as the seasonal period in the call to the *simple_configs()* function when preparing the model configurations.

# model configs cfg_list = simple_configs(max_length, offsets=[1,12])

The complete example grid searching the monthly mean temperature time series forecasting problem is listed below.

# grid search simple forecast for monthly mean temperature from math import sqrt from numpy import mean from numpy import median from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values) # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs if __name__ == '__main__': # define dataset series = read_csv('monthly-mean-temp.csv', header=0, index_col=0) data = series.values print(data) # data split n_test = 12 # model configs max_length = len(data) - n_test cfg_list = simple_configs(max_length, offsets=[1,12]) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example prints the model configurations and the RMSE are printed as the models are evaluated.

The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 1.501 degrees with the following configuration:

**Strategy**: Average**n**: 4**offset**: 12**function**: mean()

This finding is not too surprising. Given the seasonal structure of the data, we would expect a function of the last few observations at prior points in the yearly cycle to be effective.

... > Model[[227, 12, 'persist']] 5.365 > Model[[228, 1, 'persist']] 2.818 > Model[[228, 1, 'mean']] 8.258 > Model[[228, 1, 'median']] 8.361 > Model[[228, 12, 'persist']] 2.818 done [4, 12, 'mean'] 1.5015616870445234 [8, 12, 'mean'] 1.5794579766489512 [13, 12, 'mean'] 1.586186052546763

The ‘monthly car sales’ dataset summarizes the monthly car sales in Quebec, Canada between 1960 and 1968.

The dataset has an obvious trend and seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-car-sales.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('monthly-car-sales.csv', header=0, index_col=0)

The dataset has 9 years, or 108 observations. We will use the last year or 12 observations as the test set.

The period of the seasonal component could be 12 months. We will try this as the seasonal period in the call to the *simple_configs()* function when preparing the model configurations.

# model configs cfg_list = simple_configs(max_length, offsets=[1,12])

The complete example grid searching the monthly car sales time series forecasting problem is listed below.

# grid search simple forecast for monthly car sales from math import sqrt from numpy import mean from numpy import median from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step simple forecast def simple_forecast(history, config): n, offset, avg_type = config # persist value, ignore other config if avg_type == 'persist': return history[-n] # collect values to average values = list() if offset == 1: values = history[-n:] else: # skip bad configs if n*offset > len(history): raise Exception('Config beyond end of data: %d %d' % (n,offset)) # try and collect n values using offset for i in range(1, n+1): ix = i * offset values.append(history[-ix]) # check if we can average if len(values) < 2: raise Exception('Cannot calculate average') # mean of last n values if avg_type == 'mean': return mean(values) # median of last n values return median(values) # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = simple_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of simple configs to try def simple_configs(max_length, offsets=[1]): configs = list() for i in range(1, max_length+1): for o in offsets: for t in ['persist', 'mean', 'median']: cfg = [i, o, t] configs.append(cfg) return configs if __name__ == '__main__': # define dataset series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values print(data) # data split n_test = 12 # model configs max_length = len(data) - n_test cfg_list = simple_configs(max_length, offsets=[1,12]) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example prints the model configurations and the RMSE are printed as the models are evaluated.

The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 1841.155 sales with the following configuration:

**Strategy**: Average**n**: 3**offset**: 12**function**: median()

It is not surprising that the chosen model is a function of the last few observations at the same point in prior cycles, although the use of the median instead of the mean may not have been immediately obvious and the results were much better than the mean.

... > Model[[79, 1, 'median']] 5124.113 > Model[[91, 12, 'persist']] 9580.149 > Model[[79, 12, 'persist']] 8641.529 > Model[[92, 1, 'persist']] 9830.921 > Model[[92, 1, 'mean']] 5148.126 done [3, 12, 'median'] 1841.1559321976688 [3, 12, 'mean'] 2115.198495632485 [4, 12, 'median'] 2184.37708988932

This section lists some ideas for extending the tutorial that you may wish to explore.

**Plot Forecast**. Update the framework to re-fit a model with the best configuration and forecast the entire test dataset, then plot the forecast compared to the actual observations in the test set.**Drift Method**. Implement the drift method for simple forecasts and compare the results to the average and naive methods.**Another Dataset**. Apply the developed framework to an additional univariate time series problem.

If you explore any of these extensions, I’d love to know.

This section provides more resources on the topic if you are looking to go deeper.

In this tutorial, you discovered how to develop a framework from scratch for grid searching simple naive and averaging strategies for time series forecasting with univariate data.

Specifically, you learned:

- How to develop a framework for grid searching simple models from scratch using walk-forward validation.
- How to grid search simple model hyperparameters for daily time series data for births.
- How to grid search simple model hyperparameters for monthly time series data for shampoo sales, car sales, and temperature.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Grid Search Naive Methods for Univariate Time Series Forecasting appeared first on Machine Learning Mastery.

]]>The post How to Grid Search SARIMA Hyperparameters for Time Series Forecasting appeared first on Machine Learning Mastery.

]]>It is an effective approach for time series forecasting, although it requires careful analysis and domain expertise in order to configure the seven or more model hyperparameters.

An alternative approach to configuring the model that makes use of fast and parallel modern hardware is to grid search a suite of hyperparameter configurations in order to discover what works best. Often, this process can reveal non-intuitive model configurations that result in lower forecast error than those configurations specified through careful analysis.

In this tutorial, you will discover how to develop a framework for grid searching all of the SARIMA model hyperparameters for univariate time series forecasting.

After completing this tutorial, you will know:

- How to develop a framework for grid searching SARIMA models from scratch using walk-forward validation.
- How to grid search SARIMA model hyperparameters for daily time series data for births.
- How to grid search SARIMA model hyperparameters for monthly time series data for shampoo sales, car sales, and temperature.

Let’s get started.

**Updated Apr/2019**: Updated the link to dataset.

This tutorial is divided into six parts; they are:

- SARIMA for Time Series Forecasting
- Develop a Grid Search Framework
- Case Study 1: No Trend or Seasonality
- Case Study 2: Trend
- Case Study 3: Seasonality
- Case Study 4: Trend and Seasonality

Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports univariate time series data with a seasonal component.

It adds three new hyperparameters to specify the autoregression (AR), differencing (I), and moving average (MA) for the seasonal component of the series, as well as an additional parameter for the period of the seasonality.

A seasonal ARIMA model is formed by including additional seasonal terms in the ARIMA […] The seasonal part of the model consists of terms that are very similar to the non-seasonal components of the model, but they involve backshifts of the seasonal period.

— Page 242, Forecasting: principles and practice, 2013.

Configuring a SARIMA requires selecting hyperparameters for both the trend and seasonal elements of the series.

There are three trend elements that require configuration.

They are the same as the ARIMA model; specifically:

**p**: Trend autoregression order.**d**: Trend difference order.**q**: Trend moving average order.

There are four seasonal elements that are not part of ARIMA that must be configured; they are:

**P**: Seasonal autoregressive order.**D**: Seasonal difference order.**Q**: Seasonal moving average order.**m**: The number of time steps for a single seasonal period.

Together, the notation for a SARIMA model is specified as:

SARIMA(p,d,q)(P,D,Q)m

The SARIMA model can subsume the ARIMA, ARMA, AR, and MA models via model configuration parameters.

The trend and seasonal hyperparameters of the model can be configured by analyzing autocorrelation and partial autocorrelation plots, and this can take some expertise.

An alternative approach is to grid search a suite of model configurations and discover which configurations work best for a specific univariate time series.

Seasonal ARIMA models can potentially have a large number of parameters and combinations of terms. Therefore, it is appropriate to try out a wide range of models when fitting to data and choose a best fitting model using an appropriate criterion …

— Pages 143-144, Introductory Time Series with R, 2009.

This approach can be faster on modern computers than an analysis process and can reveal surprising findings that might not be obvious and result in lower forecast error.

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

In this section, we will develop a framework for grid searching SARIMA model hyperparameters for a given univariate time series forecasting problem.

We will use the implementation of SARIMA provided by the statsmodels library.

This model has hyperparameters that control the nature of the model performed for the series, trend and seasonality, specifically:

**order**: A tuple p, d, and q parameters for the modeling of the trend.**sesonal_order**: A tuple of P, D, Q, and m parameters for the modeling the seasonality**trend**: A parameter for controlling a model of the deterministic trend as one of ‘n’,’c’,’t’,’ct’ for no trend, constant, linear, and constant with linear trend, respectively.

If you know enough about your problem to specify one or more of these parameters, then you should specify them. If not, you can try grid searching these parameters.

We can start-off by defining a function that will fit a model with a given configuration and make a one-step forecast.

The *sarima_forecast()* below implements this behavior.

The function takes an array or list of contiguous prior observations and a list of configuration parameters used to configure the model, specifically two tuples and a string for the trend order, seasonal order trend, and parameter.

We also try to make the model robust by relaxing constraints, such as that the data must be stationary and that the MA transform be invertible.

# one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0]

Next, we need to build up some functions for fitting and evaluating a model repeatedly via walk-forward validation, including splitting a dataset into train and test sets and evaluating one-step forecasts.

We can split a list or NumPy array of data using a slice given a specified size of the split, e.g. the number of time steps to use from the data in the test set.

The *train_test_split()* function below implements this for a provided dataset and a specified number of time steps to use in the test set.

After forecasts have been made for each step in the test dataset, they need to be compared to the test set in order to calculate an error score.

There are many popular error scores for time series forecasting. In this case we will use root mean squared error (RMSE), but you can change this to your preferred measure, e.g. MAPE, MAE, etc.

The *measure_rmse()* function below will calculate the RMSE given a list of actual (the test set) and predicted values.

We can now implement the walk-forward validation scheme. This is a standard approach to evaluating a time series forecasting model that respects the temporal ordering of observations.

First, a provided univariate time series dataset is split into train and test sets using the *train_test_split()* function. Then the number of observations in the test set are enumerated. For each we fit a model on all of the history and make a one step forecast. The true observation for the time step is then added to the history and the process is repeated. The *sarima_forecast()* function is called in order to fit a model and make a prediction. Finally, an error score is calculated by comparing all one-step forecasts to the actual test set by calling the *measure_rmse()* function.

The *walk_forward_validation()* function below implements this, taking a univariate time series, a number of time steps to use in the test set, and an array of model configuration.

# walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error

If you are interested in making multi-step predictions, you can change the call to *predict()* in the *sarima_forecast()* function and also change the calculation of error in the *measure_rmse()* function.

We can call *walk_forward_validation()* repeatedly with different lists of model configurations.

One possible issue is that some combinations of model configurations may not be called for the model and will throw an exception, e.g. specifying some but not all aspects of the seasonal structure in the data.

Further, some models may also raise warnings on some data, e.g. from the linear algebra libraries called by the statsmodels library.

We can trap exceptions and ignore warnings during the grid search by wrapping all calls to *walk_forward_validation()* with a try-except and a block to ignore warnings. We can also add debugging support to disable these protections in the case we want to see what is really going on. Finally, if an error does occur, we can return a None result, otherwise we can print some information about the skill of each model evaluated. This is helpful when a large number of models are evaluated.

The *score_model()* function below implements this and returns a tuple of (key and result), where the key is a string version of the tested model configuration.

# score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result)

Next, we need a loop to test a list of different model configurations.

This is the main function that drives the grid search process and will call the *score_model()* function for each model configuration.

We can dramatically speed up the grid search process by evaluating model configurations in parallel. One way to do that is to use the Joblib library.

We can define a Parallel object with the number of cores to use and set it to the number of scores detected in your hardware.

executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing')

We can then can then create a list of tasks to execute in parallel, which will be one call to the *score_model()* function for each model configuration we have.

tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list)

Finally, we can use the Parallel object to execute the list of tasks in parallel.

scores = executor(tasks)

That’s it.

We can also provide a non-parallel version of evaluating all model configurations in case we want to debug something.

scores = [score_model(data, n_test, cfg) for cfg in cfg_list]

The result of evaluating a list of configurations will be a list of tuples, each with a name that summarizes a specific model configuration and the error of the model evaluated with that configuration as either the RMSE or None if there was an error.

We can filter out all scores with a None.

scores = [r for r in scores if r[1] != None]

We can then sort all tuples in the list by the score in ascending order (best are first), then return this list of scores for review.

The *grid_search()* function below implements this behavior given a univariate time series dataset, a list of model configurations (list of lists), and the number of time steps to use in the test set. An optional *parallel* argument allows the evaluation of models across all cores to be tuned on or off, and is on by default.

# grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores

We’re nearly done.

The only thing left to do is to define a list of model configurations to try for a dataset.

We can define this generically. The only parameter we may want to specify is the periodicity of the seasonal component in the series, if one exists. By default, we will assume no seasonal component.

The *sarima_configs()* function below will create a list of model configurations to evaluate.

The configurations assume each of the AR, MA, and I components for trend and seasonality are low order, e.g. off (0) or in [1,2]. You may want to extend these ranges if you believe the order may be higher. An optional list of seasonal periods can be specified, and you could even change the function to specify other elements that you may know about your time series.

In theory, there are 1,296 possible model configurations to evaluate, but in practice, many will not be valid and will result in an error that we will trap and ignore.

# create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models

We now have a framework for grid searching SARIMA model hyperparameters via one-step walk-forward validation.

It is generic and will work for any in-memory univariate time series provided as a list or NumPy array.

We can make sure all the pieces work together by testing it on a contrived 10-step dataset.

The complete example is listed below.

# grid search sarima hyperparameters from math import sqrt from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error # one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models if __name__ == '__main__': # define dataset data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] print(data) # data split n_test = 4 # model configs cfg_list = sarima_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example first prints the contrived time series dataset.

Next, the model configurations and their errors are reported as they are evaluated, truncated below for brevity.

Finally, the configurations and the error for the top three configurations are reported. We can see that many models achieve perfect performance on this simple linearly increasing contrived time series problem.

[10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] ... > Model[[(2, 0, 0), (2, 0, 0, 0), 'ct']] 0.001 > Model[[(2, 0, 0), (2, 0, 1, 0), 'ct']] 0.000 > Model[[(2, 0, 1), (0, 0, 0, 0), 'n']] 0.000 > Model[[(2, 0, 1), (0, 0, 1, 0), 'n']] 0.000 done [(2, 1, 0), (1, 0, 0, 0), 'n'] 0.0 [(2, 1, 0), (2, 0, 0, 0), 'n'] 0.0 [(2, 1, 1), (1, 0, 1, 0), 'n'] 0.0

Now that we have a robust framework for grid searching SARIMA model hyperparameters, let’s test it out on a suite of standard univariate time series datasets.

The datasets were chosen for demonstration purposes; I am not suggesting that a SARIMA model is the best approach for each dataset; perhaps an ETS or something else would be more appropriate in some cases.

The ‘daily female births’ dataset summarizes the daily total female births in California, USA in 1959.

The dataset has no obvious trend or seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘*daily-total-female-births.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('daily-total-female-births.csv', header=0, index_col=0)

The dataset has one year, or 365 observations. We will use the first 200 for training and the remaining 165 as the test set.

The complete example grid searching the daily female univariate time series forecasting problem is listed below.

# grid search sarima hyperparameters for daily female dataset from math import sqrt from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models if __name__ == '__main__': # load dataset series = read_csv('daily-total-female-births.csv', header=0, index_col=0) data = series.values print(data.shape) # data split n_test = 165 # model configs cfg_list = sarima_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example may take a few minutes on modern hardware.

Model configurations and the RMSE are printed as the models are evaluated The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 6.77 births with the following configuration:

**Order**: (1, 0, 2)**Seasonal Order**: (1, 0, 1, 0)**Trend Parameter**: ‘t’ for linear trend

It is surprising that a configuration with some seasonal elements resulted in the lowest error. I would not have guessed at this configuration and would have likely stuck with an ARIMA model.

... > Model[[(2, 1, 2), (1, 0, 1, 0), 'ct']] 6.905 > Model[[(2, 1, 2), (2, 0, 0, 0), 'ct']] 7.031 > Model[[(2, 1, 2), (2, 0, 1, 0), 'ct']] 6.985 > Model[[(2, 1, 2), (1, 0, 2, 0), 'ct']] 6.941 > Model[[(2, 1, 2), (2, 0, 2, 0), 'ct']] 7.056 done [(1, 0, 2), (1, 0, 1, 0), 't'] 6.770349800255089 [(0, 1, 2), (1, 0, 2, 0), 'ct'] 6.773217122759515 [(2, 1, 1), (2, 0, 2, 0), 'ct'] 6.886633191752254

The ‘shampoo’ dataset summarizes the monthly sales of shampoo over a three-year period.

The dataset contains an obvious trend but no obvious seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘shampoo.csv’ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

# parse dates def custom_parser(x): return datetime.strptime('195'+x, '%Y-%m') # load dataset series = read_csv('shampoo.csv', header=0, index_col=0, date_parser=custom_parser)

The dataset has three years, or 36 observations. We will use the first 24 for training and the remaining 12 as the test set.

The complete example grid searching the shampoo sales univariate time series forecasting problem is listed below.

# grid search sarima hyperparameters for monthly shampoo sales dataset from math import sqrt from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error from pandas import read_csv from pandas import datetime # one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models # parse dates def custom_parser(x): return datetime.strptime('195'+x, '%Y-%m') if __name__ == '__main__': # load dataset series = read_csv('shampoo.csv', header=0, index_col=0, date_parser=custom_parser) data = series.values print(data.shape) # data split n_test = 12 # model configs cfg_list = sarima_configs() # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example may take a few minutes on modern hardware.

Model configurations and the RMSE are printed as the models are evaluated The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 54.76 sales with the following configuration:

**Trend Order**: (0, 1, 2)**Seasonal Order**: (2, 0, 2, 0)**Trend Parameter**: ‘t’ (linear trend)

... > Model[[(2, 1, 2), (1, 0, 1, 0), 'ct']] 68.891 > Model[[(2, 1, 2), (2, 0, 0, 0), 'ct']] 75.406 > Model[[(2, 1, 2), (1, 0, 2, 0), 'ct']] 80.908 > Model[[(2, 1, 2), (2, 0, 1, 0), 'ct']] 78.734 > Model[[(2, 1, 2), (2, 0, 2, 0), 'ct']] 82.958 done [(0, 1, 2), (2, 0, 2, 0), 't'] 54.767582003072874 [(0, 1, 1), (2, 0, 2, 0), 'ct'] 58.69987083057107 [(1, 1, 2), (0, 0, 1, 0), 't'] 58.709089340600094

The ‘monthly mean temperatures’ dataset summarizes the monthly average air temperatures in Nottingham Castle, England from 1920 to 1939 in degrees Fahrenheit.

The dataset has an obvious seasonal component and no obvious trend.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-mean-temp.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('monthly-mean-temp.csv', header=0, index_col=0)

The dataset has 20 years, or 240 observations. We will trim the dataset to the last five years of data (60 observations) in order to speed up the model evaluation process and use the last year, or 12 observations, for the test set.

# trim dataset to 5 years data = data[-(5*12):]

The period of the seasonal component is about one year, or 12 observations. We will use this as the seasonal period in the call to the *sarima_configs()* function when preparing the model configurations.

# model configs cfg_list = sarima_configs(seasonal=[0, 12])

The complete example grid searching the monthly mean temperature time series forecasting problem is listed below.

# grid search sarima hyperparameters for monthly mean temp dataset from math import sqrt from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models if __name__ == '__main__': # load dataset series = read_csv('monthly-mean-temp.csv', header=0, index_col=0) data = series.values # trim dataset to 5 years data = data[-(5*12):] # data split n_test = 12 # model configs cfg_list = sarima_configs(seasonal=[0, 12]) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example may take a few minutes on modern hardware.

Model configurations and the RMSE are printed as the models are evaluated The top three model configurations and their error are reported at the end of the run.

We can see that the best result was an RMSE of about 1.5 degrees with the following configuration:

**Trend Order**: (0, 0, 0)**Seasonal Order**: (1, 0, 1, 12)**Trend Parameter**: ‘n’ (no trend)

As we would expect, the model has no trend component and a 12-month seasonal ARMA component.

... > Model[[(2, 1, 2), (2, 1, 0, 12), 't']] 4.599 > Model[[(2, 1, 2), (1, 1, 0, 12), 'ct']] 2.477 > Model[[(2, 1, 2), (2, 0, 0, 12), 'ct']] 2.548 > Model[[(2, 1, 2), (2, 0, 1, 12), 'ct']] 2.893 > Model[[(2, 1, 2), (2, 1, 0, 12), 'ct']] 5.404 done [(0, 0, 0), (1, 0, 1, 12), 'n'] 1.5577613610905712 [(0, 0, 0), (1, 1, 0, 12), 'n'] 1.6469530713847962 [(0, 0, 0), (2, 0, 0, 12), 'n'] 1.7314448163607488

The ‘monthly car sales’ dataset summarizes the monthly car sales in Quebec, Canada between 1960 and 1968.

The dataset has an obvious trend and seasonal component.

Download the dataset directly from here:

Save the file with the filename ‘*monthly-car-sales.csv*‘ in your current working directory.

We can load this dataset as a Pandas series using the function *read_csv()*.

series = read_csv('monthly-car-sales.csv', header=0, index_col=0)

The dataset has 9 years, or 108 observations. We will use the last year, or 12 observations, as the test set.

The period of the seasonal component could be six months or 12 months. We will try both as the seasonal period in the call to the *sarima_configs()* function when preparing the model configurations.

# model configs cfg_list = sarima_configs(seasonal=[0,6,12])

The complete example grid searching the monthly car sales time series forecasting problem is listed below.

# grid search sarima hyperparameters for monthly car sales dataset from math import sqrt from multiprocessing import cpu_count from joblib import Parallel from joblib import delayed from warnings import catch_warnings from warnings import filterwarnings from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error from pandas import read_csv # one-step sarima forecast def sarima_forecast(history, config): order, sorder, trend = config # define model model = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False) # fit model model_fit = model.fit(disp=False) # make one step forecast yhat = model_fit.predict(len(history), len(history)) return yhat[0] # root mean squared error or rmse def measure_rmse(actual, predicted): return sqrt(mean_squared_error(actual, predicted)) # split a univariate dataset into train/test sets def train_test_split(data, n_test): return data[:-n_test], data[-n_test:] # walk-forward validation for univariate data def walk_forward_validation(data, n_test, cfg): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # fit model and make forecast for history yhat = sarima_forecast(history, cfg) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # estimate prediction error error = measure_rmse(test, predictions) return error # score a model, return None on failure def score_model(data, n_test, cfg, debug=False): result = None # convert config to a key key = str(cfg) # show all warnings and fail on exception if debugging if debug: result = walk_forward_validation(data, n_test, cfg) else: # one failure during model validation suggests an unstable config try: # never show warnings when grid searching, too noisy with catch_warnings(): filterwarnings("ignore") result = walk_forward_validation(data, n_test, cfg) except: error = None # check for an interesting result if result is not None: print(' > Model[%s] %.3f' % (key, result)) return (key, result) # grid search configs def grid_search(data, cfg_list, n_test, parallel=True): scores = None if parallel: # execute configs in parallel executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing') tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list) scores = executor(tasks) else: scores = [score_model(data, n_test, cfg) for cfg in cfg_list] # remove empty results scores = [r for r in scores if r[1] != None] # sort configs by error, asc scores.sort(key=lambda tup: tup[1]) return scores # create a set of sarima configs to try def sarima_configs(seasonal=[0]): models = list() # define config lists p_params = [0, 1, 2] d_params = [0, 1] q_params = [0, 1, 2] t_params = ['n','c','t','ct'] P_params = [0, 1, 2] D_params = [0, 1] Q_params = [0, 1, 2] m_params = seasonal # create config instances for p in p_params: for d in d_params: for q in q_params: for t in t_params: for P in P_params: for D in D_params: for Q in Q_params: for m in m_params: cfg = [(p,d,q), (P,D,Q,m), t] models.append(cfg) return models if __name__ == '__main__': # load dataset series = read_csv('monthly-car-sales.csv', header=0, index_col=0) data = series.values print(data.shape) # data split n_test = 12 # model configs cfg_list = sarima_configs(seasonal=[0,6,12]) # grid search scores = grid_search(data, cfg_list, n_test) print('done') # list top 3 configs for cfg, error in scores[:3]: print(cfg, error)

Running the example may take a few minutes on modern hardware.

We can see that the best result was an RMSE of about 1,551 sales with the following configuration:

**Trend Order**: (0, 0, 0)**Seasonal Order**: (1, 1, 0, 12)**Trend Parameter**: ‘t’ (linear trend)

> Model[[(2, 1, 2), (2, 1, 1, 6), 'ct']] 2246.248 > Model[[(2, 1, 2), (2, 0, 2, 12), 'ct']] 10710.462 > Model[[(2, 1, 2), (2, 1, 2, 6), 'ct']] 2183.568 > Model[[(2, 1, 2), (2, 1, 0, 12), 'ct']] 2105.800 > Model[[(2, 1, 2), (2, 1, 1, 12), 'ct']] 2330.361 > Model[[(2, 1, 2), (2, 1, 2, 12), 'ct']] 31580326686.803 done [(0, 0, 0), (1, 1, 0, 12), 't'] 1551.8423920342414 [(0, 0, 0), (2, 1, 1, 12), 'c'] 1557.334614575545 [(0, 0, 0), (1, 1, 0, 12), 'c'] 1559.3276311282675

This section lists some ideas for extending the tutorial that you may wish to explore.

**Data Transforms**. Update the framework to support configurable data transforms such as normalization and standardization.**Plot Forecast**. Update the framework to re-fit a model with the best configuration and forecast the entire test dataset, then plot the forecast compared to the actual observations in the test set.**Tune Amount of History**. Update the framework to tune the amount of historical data used to fit the model (e.g. in the case of the 10 years of max temperature data).

If you explore any of these extensions, I’d love to know.

This section provides more resources on the topic if you are looking to go deeper.

- How to Create an ARIMA Model for Time Series Forecasting with Python
- How to Grid Search ARIMA Model Hyperparameters with Python
- A Gentle Introduction to Autocorrelation and Partial Autocorrelation

- Chapter 8 ARIMA models, Forecasting: principles and practice, 2013.
- Chapter 7, Non-stationary Models, Introductory Time Series with R, 2009.

- A Gentle Introduction to SARIMA for Time Series Forecasting in Python
- Statsmodels Time Series Analysis by State Space Methods
- statsmodels.tsa.statespace.sarimax.SARIMAX API
- statsmodels.tsa.statespace.sarimax.SARIMAXResults API
- Statsmodels SARIMAX Notebook
- Joblib: running Python functions as pipeline jobs

In this tutorial, you discovered how to develop a framework for grid searching all of the SARIMA model hyperparameters for univariate time series forecasting.

Specifically, you learned:

- How to develop a framework for grid searching SARIMA models from scratch using walk-forward validation.
- How to grid search SARIMA model hyperparameters for daily time series data for births.
- How to grid search SARIMA model hyperparameters for monthly time series data for shampoo sales, car sales and temperature.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

The post How to Grid Search SARIMA Hyperparameters for Time Series Forecasting appeared first on Machine Learning Mastery.

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