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Long Short-Term Memory networks, or LSTMs, are a powerful type of recurrent neural network capable of learning long sequences of observations.

A promise of LSTMs is that they may be effective at time series forecasting, although the method is known to be difficult to configure and use for these purposes.

A key feature of LSTMs is that they maintain an internal state that can aid in the forecasting. This raises the question of how best to seed the state of a fit LSTM model prior to making a forecast.

In this tutorial, you will discover how to design, execute, and interpret the results from an experiment to explore whether it is better to seed the state of a fit LSTM from the training dataset or to use no prior state.

After completing this tutorial, you will know:

- About the open question of how to best initialize the state of a fit LSTM for forecasting.
- How to develop a robust test harness for evaluating LSTM models on univariate time series forecasting problems.
- How to determine whether or not seeding the state of your LSTM prior to forecasting is a good idea on your time series forecasting problem.

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.

**Updated Apr/2019**: Updated the link to dataset.

## Tutorial Overview

This tutorial is broken down into 5 parts; they are:

- Seeding LSTM State
- Shampoo Sales Dataset
- LSTM Model and Test Harness
- Code Listing
- Experimental Results

### Environment

This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this example.

You must have Keras (version 2.0 or higher) installed with either the TensorFlow or Theano backend.

The tutorial also assumes you have scikit-learn, Pandas, NumPy, and Matplotlib installed.

If you need help setting up your Python environment, see this post:

## Seeding LSTM State

When using stateless LSTMs in Keras, you have fine-grained control over when the internal state of the model is cleared.

This is achieved using the *model.reset_states()* function.

When training a stateful LSTM, it is important to clear the state of the model between training epochs. This is so that the state built up during training over the epoch is commensurate with the sequence of observations in the epoch.

Given that we have this fine-grained control, there is a question as to whether or not and how to initialize the state of the LSTM prior to making a forecast.

The options are:

- Reset state prior to forecasting.
- Initialize state with the training datasets prior to forecasting.

It is assumed that initializing the state of the model using the training data would be superior, but this needs to be confirmed with experimentation.

Additionally, there may be multiple ways to seed this state; for example:

- Complete a training epoch, including weight updates. For example, do not reset at the end of the last training epoch.
- Complete a forecast of the training data.

Generally, it is believed that both of these approaches would be somewhat equivalent. The latter of forecasting the training dataset is preferred because it does not require any modification to network weights and could be a repeatable procedure for an immutable network saved to file.

In this tutorial, we will consider the difference between:

- Forecasting a test dataset using a fit LSTM with no state (e.g. after a reset).
- Forecasting a test dataset with a fit LSTM with state after having forecast the training dataset.

Next, let’s take a look at a standard time series dataset we will use in this experiment.

## Shampoo Sales Dataset

This dataset describes the monthly number of sales of shampoo over a 3-year period.

The units are a sales count and there are 36 observations. The original dataset is credited to Makridakis, Wheelwright, and Hyndman (1998).

The example below loads and creates a plot of the loaded dataset.

1 2 3 4 5 6 7 8 9 10 11 12 13 |
# load and plot dataset from pandas import read_csv from pandas import datetime from matplotlib import pyplot # load dataset def parser(x): return datetime.strptime('190'+x, '%Y-%m') series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) # summarize first few rows print(series.head()) # line plot series.plot() pyplot.show() |

Running the example loads the dataset as a Pandas Series and prints the first 5 rows.

1 2 3 4 5 6 7 |
Month 1901-01-01 266.0 1901-02-01 145.9 1901-03-01 183.1 1901-04-01 119.3 1901-05-01 180.3 Name: Sales, dtype: float64 |

A line plot of the series is then created showing a clear increasing trend.

Next, we will take a look at the LSTM configuration and test harness used in the experiment.

### Need help with Deep Learning for Time Series?

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## LSTM Model and Test Harness

### Data Split

We will split the Shampoo Sales dataset into two parts: a training and a test set.

The first two years of data will be taken for the training dataset and the remaining one year of data will be used for the test set.

Models will be developed using the training dataset and will make predictions on the test dataset.

### Model Evaluation

A rolling-forecast scenario will be used, also called walk-forward model validation.

Each time step of the test dataset will be walked one at a time. A model will be used to make a forecast for the time step, then the actual expected value from the test set will be taken and made available to the model for the forecast on the next time step.

This mimics a real-world scenario where new Shampoo Sales observations would be available each month and used in the forecasting of the following month.

This will be simulated by the structure of the train and test datasets. We will make all of the forecasts in a one-shot method.

All forecasts on the test dataset will be collected and an error score calculated to summarize the skill of the model. The root mean squared error (RMSE) will be used as it punishes large errors and results in a score that is in the same units as the forecast data, namely monthly shampoo sales.

### Data Preparation

Before we can fit an LSTM model to the dataset, we must transform the data.

The following three data transforms are performed on the dataset prior to fitting a model and making a forecast.

**Transform the time series data so that it is stationary**. Specifically a*lag=1*differencing to remove the increasing trend in the data.**Transform the time series into a supervised learning problem**. Specifically the organization of data into input and output patterns where the observation at the previous time step is used as an input to forecast the observation at the current timestep.**Transform the observations to have a specific scale**. Specifically, to rescale the data to values between -1 and 1 to meet the default hyperbolic tangent activation function of the LSTM model.

### LSTM Model

An LSTM model configuration will be used that is skillful but untuned.

This means that the model will be fit to the data and will be able to make meaningful forecasts, but will not be the optimal model for the dataset.

The network topology consists of 1 input, a hidden layer with 4 units, and an output layer with 1 output value.

The model will be fit for 3,000 epochs with a batch size of 4. The training dataset will be reduced to 20 observations after data preparation. This is so that the batch size evenly divides into both the training dataset and the test dataset (a requirement).

### Experimental Run

Each scenario will be run 30 times.

This means that 30 models will be created and evaluated for each scenario. The RMSE from each run will be collected providing a population of results that can be summarized using descriptive statistics like the mean and standard deviation.

This is required because neural networks like the LSTM are influenced by their initial conditions (e.g. their initial random weights).

The mean results for each scenario will allow us to interpret the average behavior of each scenario and how they compare.

Let’s dive into the results.

## Code Listing

Key modular behaviors were separated into functions for readability and testability, in case you would like to reuse this experimental setup.

The specifics of the scenarios are described in the *experiment()* function.

The complete code listing is provided below.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
from pandas import DataFrame from pandas import Series from pandas import concat from pandas import read_csv from pandas import datetime from sklearn.metrics import mean_squared_error from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from math import sqrt import matplotlib # be able to save images on server matplotlib.use('Agg') from matplotlib import pyplot import numpy # date-time parsing function for loading the dataset def parser(x): return datetime.strptime('190'+x, '%Y-%m') # frame a sequence as a supervised learning problem def timeseries_to_supervised(data, lag=1): df = DataFrame(data) columns = [df.shift(i) for i in range(1, lag+1)] columns.append(df) df = concat(columns, axis=1) df = df.drop(0) return df # create a differenced series def difference(dataset, interval=1): diff = list() for i in range(interval, len(dataset)): value = dataset[i] - dataset[i - interval] diff.append(value) return Series(diff) # invert differenced value def inverse_difference(history, yhat, interval=1): return yhat + history[-interval] # scale train and test data to [-1, 1] def scale(train, test): # fit scaler scaler = MinMaxScaler(feature_range=(-1, 1)) scaler = scaler.fit(train) # transform train train = train.reshape(train.shape[0], train.shape[1]) train_scaled = scaler.transform(train) # transform test test = test.reshape(test.shape[0], test.shape[1]) test_scaled = scaler.transform(test) return scaler, train_scaled, test_scaled # inverse scaling for a forecasted value def invert_scale(scaler, X, yhat): new_row = [x for x in X] + [yhat] array = numpy.array(new_row) array = array.reshape(1, len(array)) inverted = scaler.inverse_transform(array) return inverted[0, -1] # fit an LSTM network to training data def fit_lstm(train, batch_size, nb_epoch, neurons): X, y = train[:, 0:-1], train[:, -1] X = X.reshape(X.shape[0], 1, X.shape[1]) model = Sequential() model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') for i in range(nb_epoch): model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False) model.reset_states() return model # make a one-step forecast def forecast_lstm(model, batch_size, X): X = X.reshape(1, 1, len(X)) yhat = model.predict(X, batch_size=batch_size) return yhat[0,0] # run a repeated experiment def experiment(repeats, series, seed): # transform data to be stationary raw_values = series.values diff_values = difference(raw_values, 1) # transform data to be supervised learning supervised = timeseries_to_supervised(diff_values, 1) supervised_values = supervised.values # split data into train and test-sets train, test = supervised_values[0:-12], supervised_values[-12:] # transform the scale of the data scaler, train_scaled, test_scaled = scale(train, test) # run experiment error_scores = list() for r in range(repeats): # fit the model batch_size = 4 train_trimmed = train_scaled[2:, :] lstm_model = fit_lstm(train_trimmed, batch_size, 3000, 4) # forecast the entire training dataset to build up state for forecasting if seed: train_reshaped = train_trimmed[:, 0].reshape(len(train_trimmed), 1, 1) lstm_model.predict(train_reshaped, batch_size=batch_size) # forecast test dataset test_reshaped = test_scaled[:,0:-1] test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, 1) output = lstm_model.predict(test_reshaped, batch_size=batch_size) predictions = list() for i in range(len(output)): yhat = output[i,0] X = test_scaled[i, 0:-1] # invert scaling yhat = invert_scale(scaler, X, yhat) # invert differencing yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i) # store forecast predictions.append(yhat) # report performance rmse = sqrt(mean_squared_error(raw_values[-12:], predictions)) print('%d) Test RMSE: %.3f' % (r+1, rmse)) error_scores.append(rmse) return error_scores # load dataset series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) # experiment repeats = 30 results = DataFrame() # with seeding with_seed = experiment(repeats, series, True) results['with-seed'] = with_seed # without seeding without_seed = experiment(repeats, series, False) results['without-seed'] = without_seed # summarize results print(results.describe()) # save boxplot results.boxplot() pyplot.savefig('boxplot.png') |

## Experimental Results

Running the experiment takes some time or CPU or GPU hardware.

The RMSE of each run is printed to give an idea of progress.

At the end of the run, the summary statistics are calculated and printed for each scenario, including the mean and standard deviation.

The complete output is listed below.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
1) Test RMSE: 86.566 2) Test RMSE: 300.874 3) Test RMSE: 169.237 4) Test RMSE: 167.939 5) Test RMSE: 135.416 6) Test RMSE: 291.746 7) Test RMSE: 220.729 8) Test RMSE: 222.815 9) Test RMSE: 236.043 10) Test RMSE: 134.183 11) Test RMSE: 145.320 12) Test RMSE: 142.771 13) Test RMSE: 239.289 14) Test RMSE: 218.629 15) Test RMSE: 208.855 16) Test RMSE: 187.421 17) Test RMSE: 141.151 18) Test RMSE: 174.379 19) Test RMSE: 241.310 20) Test RMSE: 226.963 21) Test RMSE: 126.777 22) Test RMSE: 197.340 23) Test RMSE: 149.662 24) Test RMSE: 235.681 25) Test RMSE: 200.320 26) Test RMSE: 92.396 27) Test RMSE: 169.573 28) Test RMSE: 219.894 29) Test RMSE: 168.048 30) Test RMSE: 141.638 1) Test RMSE: 85.470 2) Test RMSE: 151.935 3) Test RMSE: 102.314 4) Test RMSE: 215.588 5) Test RMSE: 172.948 6) Test RMSE: 114.746 7) Test RMSE: 205.958 8) Test RMSE: 89.335 9) Test RMSE: 183.635 10) Test RMSE: 173.400 11) Test RMSE: 116.645 12) Test RMSE: 133.473 13) Test RMSE: 155.044 14) Test RMSE: 153.582 15) Test RMSE: 146.693 16) Test RMSE: 95.455 17) Test RMSE: 104.970 18) Test RMSE: 127.700 19) Test RMSE: 189.728 20) Test RMSE: 127.756 21) Test RMSE: 102.795 22) Test RMSE: 189.742 23) Test RMSE: 144.621 24) Test RMSE: 132.053 25) Test RMSE: 238.034 26) Test RMSE: 139.800 27) Test RMSE: 202.881 28) Test RMSE: 172.278 29) Test RMSE: 125.565 30) Test RMSE: 103.868 with-seed without-seed count 30.000000 30.000000 mean 186.432143 146.600505 std 52.559598 40.554595 min 86.565993 85.469737 25% 143.408162 115.221000 50% 180.899814 142.210265 75% 222.293194 173.287017 max 300.873841 238.034137 |

A box and whisker plot is also created and saved to file, shown below.

The results are surprising.

They suggest better results by not seeding the state of the LSTM prior to forecasting the test dataset.

This can be seen by the lower on average error of 146.600505 monthly shampoo sales compared to 186.432143 with seeding. It is much clearer in the box and whisker plot of the distributions.

Perhaps the chosen model configuration resulted in a model too small to be dependent on the sequence and internal state to benefit from seeding prior to forecasting. Perhaps larger experiments are required.

### Extensions

The surprising results open the door to further experimentation.

- Evaluate the effect of clearing vs not clearing the state after the end of the last training epoch.
- Evaluate the effect of predicting the training and test sets all at once vs one time step at a time.
- Evaluate the effect of resetting and not resetting the LSTM state at the end of each epoch.

Did you try one of these extensions? Share your findings in the comments below.

## Summary

In this tutorial, you discovered how to experimentally determine the best way to seed the state of an LSTM model on a univariate time series forecasting problem.

Specifically, you learned:

- About the problem of seeding the state of an LSTM prior to forecasting and ways to address it.
- How to develop a robust test harness for evaluating LSTM models for time series forecasting.
- How to determine whether or not to seed the state of an LSTM model with the training data prior to forecasting.

Did you run the experiment or run a modified version of the experiment?

Share your results in the comments; I’d love to see them.

Do you have any questions about this post?

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

Great article!! was wondering is this going to be extended for the multivariate case?

Many thanks,

Best,

Andrew

Thanks Andrew, yes I will have posts on the multivariate case soon.

Thanks Jason.

received the following error

ValueError: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size. Found: 19 samples

only changed epochs to nb_epoch=1 in the model.fit loop because otherwise i receive

TypeError: Received unknown keyword arguments: {‘epochs’: 1}

any suggestions?

Thanks

It looks like you need to update your Keras to version 2.0 or higher.

It’s a magical article. Get lots of from this article.

I’m freshman in deeplearning and keras, and i met the same problem. I notice that the length of variable “train_scaled” is 21,

train_trimmed = train_scaled[2:, :]

and the author made the train_trimmed begin index 2, so I tried to change to

train_trimmed = train_scaled[1:, :]

maybe it could work.

Thanks for the article.But I was wondering where is the shampoo-sales.csv..

You can download it from here leslie:

https://datamarket.com/data/set/22r0/sales-of-shampoo-over-a-three-year-period

istalled the latest theano and tensorflow version and received the following error. Any ideas? Thank you.

Traceback (most recent call last):

File “C:/Users/Myamoto/PycharmProjects/01.04.17_SentdexTensorflowWHD/errorfindentensorundkeras.py”, line 143, in

with_seed = experiment(repeats, series, True)

File “C:/Users/Myamoto/PycharmProjects/01.04.17_SentdexTensorflowWHD/errorfindentensorundkeras.py”, line 111, in experiment

lstm_model = fit_lstm(train_trimmed, batch_size, 3000, 4)

File “C:/Users/Myamoto/PycharmProjects/01.04.17_SentdexTensorflowWHD/errorfindentensorundkeras.py”, line 81, in fit_lstm

model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)

File “C:\Users\Myamoto\Anaconda3\lib\site-packages\keras\models.py”, line 853, in fit

initial_epoch=initial_epoch)

File “C:\Users\Myamoto\Anaconda3\lib\site-packages\keras\engine\training.py”, line 1406, in fit

batch_size=batch_size)

File “C:\Users\Myamoto\Anaconda3\lib\site-packages\keras\engine\training.py”, line 1318, in _standardize_user_data

str(x[0].shape[0]) + ‘ samples’)

ValueError: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size. Found: 19 samples

Yes, you need to split your data up differently or change the batch size.

Thank you Jason for this tutorial.

The code above shows me that ‘without-seed’ is the way to go, with my own raw_data too.

I was also able to feed the model with individual hyper parameters like batch size, neurons, epchochs etc. from a bigger batch environment.

And now my personal never ending obstacle.

If I reduce the repeats to one and say…

# split data into train and test-sets

instead of:

train, test = supervised_values[0:-12], supervised_values[-12:]

train, test = supervised_values[0:-1], supervised_values[-1:]

…I get a ‘real live prediction’ via the fitted model, of the last data value contained in my own raw data.

But I need a one step ahead forecast of unseen data.

I was able to do this with some other models, but never succeeded with the LSTM model.

How would you complement this code to make a a one step ahead prediction of unseen data?

Do not use this setup to make predictions. This setup is for evaluating model skill.

Fit the model on all data, then call model.predict(x) where x is the input required to make an out of sample prediction.

Is there an example how to finalize a LSTM-Model, including a one step ahead prediction?

Actually I thought I could use the functions fit_lstm and forecast_lstm standalone. Why not to do so?

I have:

unseenPredict = forecast_lstm(classRefSample.lstm_model, classRefSample.batch_size, X)

While storing a trained model and parameters from the experiment in an external class object.

My Problem is the structure of X.

Namely how to involve test_reshaped, test_scaled, invert_scale and inverse_difference, standalone, with a last known observation from the test partition part- which only consists of one data row.

Is there a guide how to use this methods standalone?

How can we convert test_reshaped to it’s original value?

Hi,

You have only one time series. What is he meaning of batch size>1?

If you have one sample and batch size is > 1, then it has no effect. The batch size will be 1 (as far as I know).

Thanks for this useful post,

Is it possible seed the network using fit() (on the training set, without resetting the state) instead of using predict()?

Yes. Try it and see if it makes a difference.

when set the LSTM parameter is stateful, whether the batch size needs to be 1 when predicting univariate time series

The batch size does not need to be 1.

why it gives me bad results when increasing slit data percentage.

the results should be improved?

Perhaps the model has less data to train on, or the estimate of error is now more accurate?

I really love your great articles! I found almost everything I need. Thank you so much!

I would like to use LSTM to solve the sequential data prediction problem (Multi-step forecasting/sequential to sequential ?).

It is a very simple case and has only 1 sample. The history data has 1-30 observations in sequence (one observation(value) at one step).

At the 10th step index, 10 observations (values) in sequence, predict the value at the 30th step index.

At the 11th step, 11 observations in sequence (a new observation), predict the value at the 30th step…..

Is it a seq2seq problem or multi-step forecasting?..

As you mentioned, we should Tune the LSTM or seed State to find a good LSTM model before making prediction. I bought the ebook ” long short term memory networks with python” and “deep learning time series forecasting”. But I didn’t find a complete code for Muti-step forecasting/seq2seq that contains Tune LSTM model, Seed state before forecasting and update model when a new observation is available ( all processes to make relatively accurate prediction).

I wonder if you have such an example somewhere (blog or ebook), but I didn’t find it…..

Thank you so much!

Thanks.

This will help as a first step:

https://machinelearningmastery.com/faq/single-faq/how-do-you-use-lstms-for-multi-step-time-series-forecasting

Then perhaps follow this process:

https://machinelearningmastery.com/start-here/#deep_learning_time_series