Last Updated on August 5, 2019

It can be hard to prepare data when you’re just getting started with deep learning.

Long Short-Term Memory, or LSTM, recurrent neural networks expect three-dimensional input in the Keras Python deep learning library.

If you have a long sequence of thousands of observations in your time series data, you must split your time series into samples and then reshape it for your LSTM model.

In this tutorial, you will discover exactly how to prepare your univariate time series data for an LSTM model in Python with Keras.

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Let’s get started.

## How to Prepare Time Series Data

Perhaps the most common question I get is how to prepare time series data for supervised learning.

I have written a few posts on the topic, such as:

- How to Convert a Time Series to a Supervised Learning Problem in Python
- Time Series Forecasting as Supervised Learning

But, these posts don’t help everyone.

I recently got this email:

I have two columns in my data file with 5000 rows, column 1 is time (with 1 hour interval) and column 2 is bits/sec and I am trying to forecast bits/sec. In that case can you please help me to set sample, time step and feature [for LSTMs]?

There are few problems here:

- LSTMs expect 3D input, and it can be challenging to get your head around this the first time.
- LSTMs don’t like sequences of more than 200-400 time steps, so the data will need to be split into samples.

In this tutorial, we will use this question as the basis for showing one way to specifically prepare data for the LSTM network in Keras.

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## 1. Load the Data

I assume you know how to load the data as a Pandas Series or DataFrame.

If not, see these posts:

Here, we will mock loading by defining a new dataset in memory with 5,000 time steps.

1 2 3 4 5 6 7 8 9 10 |
from numpy import array # load... data = list() n = 5000 for i in range(n): data.append([i+1, (i+1)*10]) data = array(data) print(data[:5, :]) print(data.shape) |

Running this piece both prints the first 5 rows of data and the shape of the loaded data.

We can see we have 5,000 rows and 2 columns: a standard univariate time series dataset.

1 2 3 4 5 6 |
[[ 1 10] [ 2 20] [ 3 30] [ 4 40] [ 5 50]] (5000, 2) |

## 2. Drop Time

If your time series data is uniform over time and there is no missing values, we can drop the time column.

If not, you may want to look at imputing the missing values, resampling the data to a new time scale, or developing a model that can handle missing values. See posts like:

- How to Handle Missing Timesteps in Sequence Prediction Problems with Python
- How to Handle Missing Data with Python
- How To Resample and Interpolate Your Time Series Data With Python

Here, we just drop the first column:

1 2 3 |
# drop time data = data[:, 1] print(data.shape) |

Now we have an array of 5,000 values.

1 |
(5000,) |

## 3. Split Into Samples

LSTMs need to process samples where each sample is a single time series.

In this case, 5,000 time steps is too long; LSTMs work better with 200-to-400 time steps based on some papers I’ve read. Therefore, we need to split the 5,000 time steps into multiple shorter sub-sequences.

I write more about splitting up long sequences here:

- How to Handle Very Long Sequences with Long Short-Term Memory Recurrent Neural Networks
- How to Prepare Sequence Prediction for Truncated Backpropagation Through Time in Keras

There are many ways to do this, and you may want to explore some depending on your problem.

For example, perhaps you need overlapping sequences, perhaps non-overlapping is good but your model needs state across the sub-sequences and so on.

Here, we will split the 5,000 time steps into 25 sub-sequences of 200 time steps each. Rather than using NumPy or Python tricks, we will do this the old fashioned way so you can see what is going on.

1 2 3 4 5 6 7 8 9 |
# split into samples (e.g. 5000/200 = 25) samples = list() length = 200 # step over the 5,000 in jumps of 200 for i in range(0,n,length): # grab from i to i + 200 sample = data[i:i+length] samples.append(sample) print(len(samples)) |

We now have 25 sub sequences of 200 time steps each.

1 |
25 |

If you’d prefer to do this in a one liner, go for it. I’d love to see what you can come up with.

Post your approach in the comments below.

## 4. Reshape Subsequences

The LSTM needs data with the format of [samples, time steps and features].

Here, we have 25 samples, 200 time steps per sample, and 1 feature.

First, we need to convert our list of arrays into a 2D NumPy array of 25 x 200.

1 2 3 |
# convert list of arrays into 2d array data = array(samples) print(data.shape) |

Running this piece, you should see:

1 |
(25, 200) |

Next, we can use the *reshape()* function to add one additional dimension for our single feature.

1 2 3 4 |
# reshape into [samples, timesteps, features] # expect [25, 200, 1] data = data.reshape((len(samples), length, 1)) print(data.shape) |

And that is it.

The data can now be used as an input (X) to an LSTM model.

1 |
(25, 200, 1) |

## Further Reading

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

### Related Posts

- How to Convert a Time Series to a Supervised Learning Problem in Python
- Time Series Forecasting as Supervised Learning
- How to Load and Explore Time Series Data in Python
- How To Load Machine Learning Data in Python
- How to Handle Missing Timesteps in Sequence Prediction Problems with Python
- How to Handle Missing Data with Python
- How To Resample and Interpolate Your Time Series Data With Python
- How to Handle Very Long Sequences with Long Short-Term Memory Recurrent Neural Networks
- How to Prepare Sequence Prediction for Truncated Backpropagation Through Time in Keras

### API

## Summary

In this tutorial, you discovered how to convert your long univariate time series data into a form that you can use to train an LSTM model in Python.

Did this post help? Do you have any questions?

Let me know in the comments below.

Great article! I wish I had this a couple months ago when I was struggling with doing the same thing for Tensorflow. Glad to see the solution I had mostly aligns with yours.

You mention some papers that discuss optimal sample size. Would you be able to share a link to those? I’m interested to see how the authors arrive at that number.

Thanks.

Perhaps check this post:

https://machinelearningmastery.com/much-training-data-required-machine-learning/

This publication helped me a lot! I really want to thank you for the post. Very simple and straight forward.

I’m happy to hear that!

Hi Jason, thx for sharing.

let say I have a timeseries dataset [1,2,3,4,5,6,7,8] and need to split it with time steps of 4, in your article, the result will be [1,2,3,4], [5,6,7,8]. But in some other articles I’ve read, the result sometime will be is this way: [1,2,3,4], [2,3,4,5],[3,4,5,6],[4,5,6,7],[5,6,7,8].

so what will be the best way to split the samples? thx.

All 3 approaches you have listed are valid, try each and see what works best for your problem.

Is there litterature on the subject? The 3 solutions seem to have a very distinct training time for large datasets. I assume that for the second solution we should keep the memory for the cell, but not for the third, right?

Also, is there a risk that the training overexposed certain timesteps(timestep 5 in the example) in early learning, giving a bigger weight to this data.

BTW great blog and your book on LSTM is the best I found on the subject. thx.

Not really.

I would suggest framing the problem each of the 3 ways and compare them to see what works best for your specific data.

Perhaps this post will help you with reframing the problem:

https://machinelearningmastery.com/reshape-input-data-long-short-term-memory-networks-keras/

When the original univariate time series gets split into a list of subsequences with length as m, with delay between each successive subsequence as d, this forms a new samples of with m dimension input vectors. This is called Takens embedding. When d = m = 4, this is the first case. When d = 1, m = 4, this is the 2nd case. As a matter of fact, any d > 1 is valid and the same goes for m. There are multiple methods available for determine “optimal” values of d and m. Here are some of publications on the subject:

https://arxiv.org/pdf/1605.01571.pdf

https://file.scirp.org/pdf/JMP_2017083015084865.pdf

Interesting, thanks for the refs.

Nice article. One thing I live about Python is list comprehension. One possible one-liner could be

samples = [data[i:i+length] for i in range(0,n, length)]

Nice, thanks.

Went, what you want is called “sliding window”, you could get it in the next code:

from itertools import islice

def window(seq, n=2):

“Returns a sliding window (of width n) over data from the iterable”

” s -> (s0,s1,…s[n-1]), (s1,s2,…,sn), … ”

it = iter(seq)

result = tuple(islice(it, n))

if len(result) == n:

yield result

for elem in it:

result = result[1:] + (elem,)

yield result

Hi Jason! First, I have to say that I really like your posts, they are very helpful.

I’m facing a time series classification problem (two classes) where I have series of around 120-200 time steps and 7 variables each. The problem is that I have only 3000 samples to train. What do you think, Is it feasible a priori to feed a LSTM network or I need more samples?

You mention that LSTM doesn’t work well with more than 200-400 timesteps. What about the number of features? Would you do dimensionality reduction?

Thank you very much in advance!

LSTMs can support multiple features.

It does not sound like enough data.

You could try splitting the sequence up into multiple subsequences to see if that helps?

Hi Jason,

Thank you for this excellent summary, your work is really impressive…I’m especially impressed by how many blog posts you have taken the time to write.

I was wondering why an LSTM network prefers a sequence of 200 – 400 samples, is this due to a memory allocation issue? Or can a longer sequence affect accuracy (I wouldn’t guess this but perhaps it’s possible)?

What role does the batch size play here? Couldn’t this restriction in sequence length be mitigated by selecting a correct batch size?

BR

Staffan

It seems to be a limitation on the training algorithm. I have seen this issue discussed in the literature, but have not pushed hard to better understand it myself.

I’d encourage you to test different configurations on your problem.

Hi jason,

Nice post! a little confused about the “time-steps” parameter. The “time-steps” means the steps span of input data? For example, for univariate problem,and one-step forecasting, i constructed the data with “sliding window”. For each sample,the structure is “t-6,t-5,t-4,t-3,t-2,t-1,t for input(train_x),and t+1 for output(train_y) ” .Using 7 data to forecast to the 8th. i reshaped the input(train_x) as [samples, 7,1]. Is that right?

Learn more about time steps in this post:

https://machinelearningmastery.com/gentle-introduction-backpropagation-time/

I think so.

I think time steps argument is for the number of rows, like features is for number of arguments as explained in one of Jason’s posts

Sorry, features is for number of columns

See this:

https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Hello Jason, sorry for my english. I’m new to neural nework and i am trying to develop a neural network to generate music.

I have many .txt file with a sequence of notes like these

[int(note number), int(time), int(length)]

68 2357 159,

64 2357 260,

…

…

What kind of neural network I have to choose for this purpose?

How can i preprocess this kind of data?

Congratulations for this website and thank you.

For sequence prediction, perhaps RNNs like the LSTM would be a good place to start.

hi

I want to classify classes each class consists of 8_time steps in each time steps 16 features. is this reshape correct

reshape(124,8,1)

I think it would be (?, 6, 16) where “?” is the number of samples, perhaps 124 if I understand your case.

Hello, Jason, thanks for the great work.

I’ve read your articles about organizing the data for LSTM in 3D, but I can not do this with my data, it always presents an error like this:

“Error when checking target: expected dense_363 to have 2 dimensions, but got array with shape (3455, 1, 1)”

My data is organized as follows:

Appetizer:

11,000 lines with 48 columns, each row represents one day and each column represents 0.5h,

The output Y (0, 1) is binary, it represents the occurrence of an event 1 = yes, 0 = no.

So I have X = [0.1, 0.2, 0.3, …, 0.48] Y = [0] or Y = [1]

for more details see my code:

# load data

dataframe = pd.read_csv(‘Parque_A_Classificado_V2.csv’, header=None)

dataset = dataframe.values

# split data to variables train and test

train_size = int(len(dataset) * 0.7)

test_size = len(dataset) – train_size

trainX, trainY = dataset[0:train_size,:48], dataset[train_size:len(dataset),48]

testX, testY = dataset[0:test_size, :48], dataset[test_size:len(dataset), 48]

# reshape input to be [samples, time steps, features]

trainX = trainX.reshape(trainX.shape[0],trainX.shape[1], 1)

testX = testX.reshape(testX.shape[0], testX.shape[1], 1)

trainY = trainY.reshape(trainY.shape[0], 1, 1)

testY = testY.reshape(testY.shape[0], 1, 1)

#criando modelo

model = Sequential()

model.add(LSTM(100, input_shape=(48, 1)))

model.add(Dense(1, activation=’sigmoid’))

# Compile model

model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘acc’])

model.fit(trainX, trainY, validation_data(testX, testY), epochs=1, batch_size=1)

I can not find the error, can you help me?

Maybe this post will make it clearer:

https://machinelearningmastery.com/reshape-input-data-long-short-term-memory-networks-keras/

Many thanks, Jason, your attitude is commendable.

This time I had to run my model.

Glad to hear you worked out your problem.

Hi Jason,

I’m struggling with a problem similar to those described here with a slight difference.

I’m solving a disaggregation problem and so my the dimensions of my output are higher than my input. in order to simplify lets say my original data looks something like this:

X.shape == [1000,1]

Y.shape == [1000,10]

I do some of the input to make things work:

X = X.reshape(1,X.shape[0[,X.shape[1]) #leaving this parameter dependent in case I want to

later use more features

My net looks like this:

model.sequential()

model.add(LSTM(50,batch_input_shape = X.shape, stateful = True)

model.add(Dense(Y.shape[1],activation = ‘relu’) #my output values aren’t between +/-1 so I

chose relu

went with a stateful model because I will most likely have to do batch seperation when running my actual training as I have close to a 10^6 samples

and then I’ve tried both doing the same thing to the Y vector and not touching it, either way I get error (when I reshaped Y I then changed Y.shape[1] to Y.shape[2])

Any thoughts?

Output will be 2D not 3D.

How can I split the 5000-row dataset into train and test portions when I am dividing into samples and reshaping it?

You could split before or after reshaping.

This post will teach you more about how to work with arrays:

https://machinelearningmastery.com/index-slice-reshape-numpy-arrays-machine-learning-python/

Thanks to this article and the one about reshaping input data for LSTM, I understood how to split/reshape the inputs of the LSTM network but I can’t see how to handle labels…

My dataset is 3000 time steps and 9 features. As explained in the article, I split it to get 15 samples of 200 time-steps so my input shape is (15, 200, 9).

My labels are a binary matrix (3000, 6) i.e. I want to solve a 6-class classification problem.

If I feed the labels as is, I’ll get an error “Found 15 input samples and 3000 target samples”.

How to correctly feed the labels to the network? What confuses me is that the targets should be 2D (unlike inputs) so I don’t see how I could split them in the same way as inputs, for example to get a (15, 200, 6) shape…

You will need one label per input sample.

Great blog thank you!

From what I understand you showed how to handle one long time series, but I couldn’t understand what to do with multiple inputs.

For example my input is x1 with dimensions (25, 200, 1)

but I have multiple inputs for my training X = [x1,x2…xn]

How should I shape for model.fit and for the LSTM layers? a 4D tensor?

I explain more here:

https://machinelearningmastery.com/faq/single-faq/how-do-i-prepare-my-data-for-an-lstm

Thank you for the wonderful blog.

Where does the total number of samples to train go in the reshape?

As I understood: (num of subsamples, time stamps, features per timestamp)

Correct: [samples, timesteps, features].

I love all your posts!

Im a bit confused:

I would guess that the number of time steps limits the number of recurrent layers. Since the number of time steps is equivalent to the amount of time steps you run your recurrent neural network. Is this true? If yes how can the memory of the LSTM be larger than the amount of recursions?

And if it isnt larger, why would anybody choose time steps = 1 like you did in some posts?

Thanks.

The time steps and the nodes/layers are unrelated.

Sorry, I fomulated my question badly.

I meant: if I have a sample sequence of lets say 100 time steps, can the memory of the LSTM be greater than these 100 time steps?

Is the memory limited by the amount of time steps given in a sequence?

Thanks for your time. T

The limit for the LSTM seems to be about about 200-400 time steps, from what I have read.

Hi Jason,

Can you please explain what you mean by LSTM does not work well for 200-400 time steps, while you replied to Daniel Salvador that 3000 training samples are not enough?

Does 200-400 mean 200-400 steps ahead prediction?

How many number of training samples you think is fairly enough?

The input data has a number of time steps. The LSTM performance appears to degrade if the number of input time steps is more than about 200-400 steps, according to the literature.

I have not tested this in experiments myself though.

Dear Jason,

Could you help me with this:

I have many phrases, and each phrases contains many words (I have padded so that they are of the same length), and I have trained word embedding for each word. So, in this case, if I want to use LSTM in keras to do some classification task (e.g. each phrase is labeled as 1 or 0, it’s related to the order of words), what will be my input shape for the LSTM layer in this case? Is it like shape (#of phrases, #of words in phrase, # of dimension of word embedding) ? I am a little confused here. Thanks for your help.

Probably: [total phrases, phrase length, 1]

Thanks for your reply. But I’m still confused here.

1. Why it is “1” at last?

2. I think the shape of my input numpy arrary (which will be thrown into Keras sequential model, whose first layer is a LSTM layer) is (#of phrases, #of words in phrase, # of dimension of word embedding). Does is mean that the input shape is (#of words in phrase, # of dimension of word embedding)? Because I want to learn something based on the sequence order between words.

My task is very similar to the task in one of your post. https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

In that post, the original input is a vector of words. Then, it will be put into a keras sequential model, however, the first layer is a Word Embedding layer, then followed by the LSTM layer. The output shape of word embedding layer should be a (2D) array, right?. Does that means the input shape of LSTM in this case is 2D rather than 3D? If it’s not, what will be the input shape in that case.

Thanks for your help.

Because you have a 1d sequence of integers to feed into your model, e.g. as input to the embedding.

The word embedding will handle its own dimensionality, don’t reshape data for it.

Hi Jason, could you help me on this?

My dataset has not been collected continuously, but it’s the result of many experiments, each one representing a specific class that I want my LSTM model to learn and predict.

Which is the best strategy to prepare the sequences for the training phase?

Should I concatenate all timeseries available and then use a sliding window to generate the sequences? in this case I may risk to have data of different classes in the same sequence…

Or would it be better to create the sequences separately for each individual class?

Thanks in advance

Perhaps brainstorm 3-5 different ways to frame the prediction problem, then prototype a few. This will help you clarify what the right approach might be.

Hi Jason,

Super post!

You did not do things like the following in your multivariate time seriies of PM 2.5 exmaple at https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/ .

# split into samples (e.g. 5000/200 = 25)

samples = list()

length = 200

# step over the 5,000 in jumps of 200

for i in range(0,n,length):

# grab from i to i + 200

sample = data[i:i+length]

samples.append(sample)

print(len(samples))

Is it becasue that PM2.5 example assumes overlaping subsequences? Or would you have any other reasons?

For your convenience, you have the following snippets in that PM2.5 example:

# reshape input to be 3D [samples, timesteps, features]

train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))

test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))

print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

(8760, 1, 8) (8760,) (35039, 1, 8) (35039,)

After I change the n_hours=3, i.e., the timesteps, I have the following output in my Spyder:

(8760, 3, 8) (8760,) (35037, 3, 8) (35037,)

Train on 8760 samples, validate on 35037 samples

This means they are overlapping subsequences.

Please let me know if I get it right or not.

Many thanks.

Because I try to keep tutorials simple and focused.

Thanks for your reply. You did not elaborate in this tutorial on when one needs overlapping subsequences, when not. Would you have a tutorial about that, or any tips?

It really depends on the problem, e.g. the composition of the input samples is defined by what you want the model to learn or map to the output sample.

This is awesome (as is your entire series)! I consistently find your articles concise, clear and lucid, so thank you.

A small suggestion about the LSTM series however- you could add a couple of lines about the shaping of Y and the return sequence option. I struggled with it earlier, despite reading all your LSTM articles so it would probably help others!

The return sequence will be a one value per input time step, per node in the layer.

E.g. the layer gets 10 time steps of 1 variable and the layer has 100 nodes, you will get [100, 10, 1].

Hii Jason, I have related question:

I have aggregated power consumption of house and individual power consumption of appliance (example: dish washer). Here, Aggragated power is my training set and appliance power consumption is target set.

Do i need to reshape both for training my model, just like the way you did in this article?

Perhaps. LSTMs have a specific expectation when it comes to the shape of input, see this:

https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Would you provide example of the shape of label data? what should be the dimension? can we train on 24 samples and predict the 25th sample?

Here’s an example of making a prediction:

https://machinelearningmastery.com/make-predictions-long-short-term-memory-models-keras/

Hi,

I just wanna say you have awesome articles!

Here is my question:

Let’s say we split the data into a shape

(100, 60, 5)

Meaning 100 samples, each of them looking 60-time steps back and 5 features.

Would I be correct to assume that after we split the data as described, we could now shuffle the 100 samples as we wished and the result would be the same.

So we could apply normal cross-validation which is otherwise not possible with RNNs?

Thanks.

No, you cannot shuffle the samples. You must use walk-forward validation:

https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/

Hi Jason! First, I have to say that I really like your posts, they are very helpful.

I have some questions about TimeSeries, would you give me some suggestion ?

1. Suppose the data: t1, t2,…t10, I prepare the data by rolling window, the window size is 3, such as [t1,t2,t3] -> t4, Then i trained a LSTM model, I want to know how to predict one time step in future ? for example: predict value on time t20 in future, but the histiry feature [t17, t18, t19] is null.

2. DO i need to prepare my data by rolling window if every timestep has a label? such as binary classify problem:

t1, f11, f12, f13, 1

t2, f21, f22, f23, 0

….

tn, fn1, fn2, fn3, 1

When i train LSTM, i reshape N time samples [N, 3] to [-1, timesteps, 3], N is number of time samples, shape of train data feed to LSTM is [-1, timesteps, 3], but this require N must equal to k * timesteps, for exampe, [60, 3] -> [-1, 12, 3] will be Ok, but [50, 3] -> [-1, 12, 3] will be wrong. I want to know how to process last 2 time sampes, should i pad zeros vector to get a sequence size 12 ?

Thank you very very much.

I have many example, you can get started here:

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

Hi, Jason. Thanks for your post.

I have some question. Suppose I have to forecasting the number of people in one region. We divide the region into 3×3 grid, each grid has the value of the current number of people. Then every one hour time interval there are 3×3 matrix, for example 8:00 there are 3×3 matrix, 9:00 there are 3×3 matrix, our goal is to use the previous two time interval (i.e. 10:00 and 11:00) to forecasting the next two interval (i.e. 10:00 and 11:00) numbers of people. How should I to deal this task. Thanks!

You can use a CNN-LSTM or ConvLSTM for read in a matrix time series, then use an encoder-decoder model to output multiple steps.

Every thing clear, but I’ve a problem.

The line code:

data = array(samples)

doesn’t return (number_samples, time_steps), but only (number_samples,), consequently the reshape instruction doesn’t work.

How can I solve this problem?

Sorry that you’re having trouble, perhaps this will help:

https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me

Thanks a lot for this blog. I am a complete newbie in this field, ( I am a PhD student in Mechanical Engg), and want to ask you for your opinion on how to handle spatio-temporal data. Let me explain, I am have some simulation results of fluid flow in a 2-d domain, the location of each point of the domain has specific x and y coordinates, has specific velocities, pressure, vorticities etc. For each time-step I have a separate data file, each data file contains multiple rows and columns. Each row corresponds to a specific point on the domain, and each column has the x-coordinate, y-coordinate of the point, the velocities, vorticities at the point, etc. What type of neural network would you suggest for this problem? I would also be very helpful if you can point me to a post which deals with similar situations if you are aware of such posts or blog?

Once again. Please accept my thank-you for the immense help I have received from your blog posts

I forgot to add:

With the data for multiple time-steps I would like to train a model which would predict the velocities and vorticity at each point for future time-steps. Again, Each time-step has separate files containing multiple data-points. The locations of each specific point remains unchanged with time.

Great question!

If the data is spatially related, e.g. like a time series of 2d images, then I would recommend looking into CNN-LSTMs and ConvLSTMs.

Thanks for your response, I will look into CNN-LSTMs. One more question. How can I feed data of multiple time-steps when each time-step has a separate file containing multiple rows? The examples I find all have each time-step has a single row in a data file. But my data has multiple time-steps each time-step with a single file.

Perhaps load all the data into memory or use a data generator to load the data progressively.

Very nice tutorial. I have a question about y_train.

My X_train has a shape of (958, 75, 10) after applying this tutorial. However, my y_train is just (71850, 9) which is a long array containing a one_hot_encoder vector of 9 possible classes.

I don’t understand how to reshape y_train to be ready for the LSTM model in Keras.

The input and output elements of samples must match up.

E.g. if you have 958 input samples, you need 958n output elements for those same samples.

Thanks for the answer. To clarify, that means that for each batch I only need one one_hot_encoder vector? Because samples I have in both 71850, but then I reshape that using batches of 75 samples. Then I would need only to catch one vector [1,0,0,0,0,0,0,0,0,0] every 75 samples?

I don’t follow, sorry. I don’t know where one hot encoding or batches came into the picture.

Hi Jason,

I just did the question in StackOverflow maybe it is more clear:

https://stackoverflow.com/questions/55983867/what-is-the-correct-format-of-y-train-when-the-output-is-one-of-8-classes-using

Perhaps you can summarize it in a sentence or two?

Hello, I have a new question as updating the last one.

If I’m correct the shape of y_train depends on the Model if I train a stateful model, it has to be (958, 9) however if I train non-stateful model it has to be (958, 75, 9)?

Hello Dr. Brownlee,

Is it possible to send vectors of vectors into LSTM? I have the word tokens (seq 1) “hello” “how” “are” “you” etc… each word is represented as a vector in word2vec. SO I get vector of vectors. This is for the seq 1. I have upto seq n. How can I use these as input to LSTM?

Yes, typically we use an embedding to store the vectors. You can provide the vectors directly to the LSTM, each each element in the vector is a feature.

Therefore you will have a sequence (words or timesteps) of feature vectors (features or embedding).

great tutorial, i have a question

so if i have a single dimensional data, should i have to make it become two dimensional data by adding the time steps?

so it become for example:

[1 20]

[2 25]

[3 30]

.

.

.

[10 65]

thank you

best regards

Good question, this will help you understand:

https://machinelearningmastery.com/time-series-forecasting-supervised-learning/

Great resource, but I am still confused about something. I have a classification problem with 5 features but i want to train the model to recognize the sequence of timesteps for each label. That is to say that sequence_feature1 + sequence_feature2 + sequence_feature3 + sequence_feature4 + sequence_feature5 = label. Is there a way to amend the LSTM input to account for this? Otherwise it seems like I am training the model that feature1+feature2 +feature3 + feature4 +feature5 = label at each timestep (which is not correct). Thanks

I don’t follow the structure of your problem sorry.

If you want memory of prior predictions, the model will have some internal state – at least until that state is reset.

The finalized data you prepared as 3D numpy array is non-overlap right?

So, the non-overlap means that there are some points we cannot predict.

In the example ( [samples, timesteps, feature] = [25, 200, 1] ),

・Predict value at 201 by using values at 1-200.

・Predict value at 401 by using values at 201-400, and so on.

So , we cannot predict, say, 202 or 207, right. Because there is no overlapping.

Is my understanding correct?

You can design the problem framing any way you wish.

Overlapping is preferred generally, it really depends on how you want to use the model in practice.

Thanks very much for the answer.

You’re welcome.

Thank you, Jason, for the great post. Following your post we end up with the following data shape

(25, 200, 1). Now, let’s imagined our model is trained. Our model now expects an input of ( variable , 200, 1). What happens if I have a single data point and I want to predict the binary output (positive/negative).

Even reshaping it, it will end up as (1, 1, 1) and the second dimension will make the model crash because it expects 200 lines per batch. Should I just adjust to the second dimension and fill the rest with null values? (1, 200, 1) The first line will be my data point but the rest will be null rows?

You’re welcome.

Perhaps this will help:

https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Hello Jason,

I appreciate your article, and I have a question on LSTM:

When you feed (25, 200, 1) into an LSTM layer, will there be 200 LSTM processors(where input, forget gate exist) in this LSTM layer to process inputs starting from 10, 20, then all the way to 2,000 and memorize?

For your multivariate example with the input shape of (8760, 1, 8), will there be only 1 LSTM processor because the timestep is 1? Would LSTM be effective since there is only one timestep to remember?

Thanks!

No.

Number of time steps and number of units in the layer are different.

See this:https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Hello Jason,

thank you for nice learning materials. I would like to ask you how would you proceed if you would have multiple 5000 series (for example from multiple different observations). For example if you would have got 1000 measures, 5000 each and you would not care about the individual ones because you would like to learn some general pattern (so there is no point to differ between them in features)?

Thanks!

Good question, this will give you ideas:

https://machinelearningmastery.com/faq/single-faq/how-to-develop-forecast-models-for-multiple-sites

Thank you for the reply, but the answers in link still takes in mind some individuality. Take as an example car speed prediction from some sensors reading (for example accelerometer). You can have thousands of sessions where you don’t care what car or sensor it was. Everything that interests you is session (that have some beginning and the end), accelerometer readings and speed. And you have thousands of really long sessions.

Does in this case make sense for example training stateful LSTM per session (the session could be time serie, chopped to smaller segments, which will serve as inputs for prediction – as it is in the article) and run training in loop through all sessions with resetting states in between?

I am trying to wrap my head around recurrent networks and most of the articles are based on maybe little bit oversimplified cases.

Thanks

Probably not, you must start with a really strong definition of what you want to predict and what might be useful as input to making the prediction:

http://machinelearningmastery.com/how-to-define-your-machine-learning-problem/

OK, so taking my example – I got source data in format [2000, 30, 250, 3] – 2000 sequences, each splitted to 30 time series with length 250 and 3 feature (reading from accelerometer). For each of time series I want to predict speed.

How to train on these data?

for i in range(2000]):

model.fit(X[i], y[i], epochs=1, verbose=0, batch_size=1)

with X[0] in shape 30x250x3 and y[0] 30×1 (if this is many to one)?

Sorry for bothering you, but as I said, I have never seen example using data like this, so I’m not sure how to approach it.

Than again for your help.

It depends how the 300 time series for each sample are related.

If they are unrelated, you have 2000*300 samples.

If they are related, perhaps the above, or perhaps fit a separate model on each.

Or perhaps a convlstm or cnn lstm.

OK, thank you for answers. I was just not sure, if I am not missing something.

You’re welcome.

Sorry, features is for number of columns

Hey Jason, I love your way of explanation but I have a doubt

I want to predict using LSTM but i am facing problems. Here is my code

def predict(self) -> list:

“””

A method to predict using the test data used in creating the class

“””

yhat = []

if(self.train_test_split > 0):

# Getting the last n time series

_, X_test, _, _ = self.create_data_for_NN()

# Making the prediction list

yhat = [y[0] for y in self.model.predict(X_test)]

return yhat

def predict_n_ahead(self, n_ahead: int):

“””

A method to predict n time steps ahead

“””

X, _, _, _ = self.create_data_for_NN(use_last_n=self.lag)

# Making the prediction list

yhat = []

for _ in range(n_ahead):

# Making the prediction

fc = self.model.predict(X)

yhat.append(fc)

# Creating a new input matrix for forecasting

X = np.append(X, fc)

# Ommiting the first variable

X = np.delete(X, 0)

# Reshaping for the next iteration

X = np.reshape(X, (1, len(X), 1))

return yhat

Can you suggest me a better way to predict ?

Because in the predict() function, there is recursion which is a little difficult for me to wrap my mind around

Sorry, I don’t have the capacity to review/debug your code.

Perhaps you can summarize your problem as a question in a sentence or two?

So if you could make any sense of my code, I actually want a shorter and easier method to make predictions

Please Jason, I need your help

I’m happy to answer specific questions about machine learning or the tutorials.

So can you tell me ,perhaps with an example code, how to use keras evaluate() ?

Yes, you can see 100s of examples on the blog, perhaps start here:

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

I tried to reshape with my own dataset which has 38881 rows and 7 columns. 8 if you count Target variable as my problem is a classification problem.

I’m having such a hard time with this step.

Your example did not work for me as my dataset is already a 2d array (I think).

How do i convert my dataset to a 3d dataset?

Thank you for your help

Perhaps this will help:https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Hi. Thank you for the reply. It did help somewhat, but when i attempt data.reshape (x, x ,x) my array cannot be reshaped into my desired 3d array as the numbers are not devidable without getting decimals. Any way around this? e.g. the last 2d array doesn’t has to be full?

Perhaps reshape using numbers that are factors for your data.

Hi Jason. Thanks for the guide, it is the only one clear I have found so far.

However, I am not able to run my case.

The main problem it seems when it comes to prediction.

I am trying to predict the response of a non-linear system using time sequences (it is a non-linear mass-spring system forced by f(t)=cost*sin(omega*t)).

From what you said in this article, so I have:

– 2 samples (input force at 2 different omega values).

– Each sample contains 2000 time steps.

– 1 feature (the input force is the only input I give to my model)

=> (2,2000,1) is the input tensor.

I gave input_shape=(2000,1) to the LSTM layer.

This dataset trains the model in a quite satisfactory way. But :

1- I am not able to use validation_data of shape (1,2000,1). It corresponds to an input force at a different omega than training set.

2- I am not able to predict the response of the system at a different omega input force. Same shape as for validate but used a different omega for the input force

Could you help me understanding where I am wrong?

I get the following error on prediction code line:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Specified a list with shape [2,1] from a tensor with shape [1,1]

Hope you can help! Thanks in advance.

2k time steps is too many. Try to limit it to 200-400.

Perhaps this will help:https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

Hi Jason, I have already read it and I have set the data dimensions according to it. I am asking how validation data and the input data must be if I train using more than 1 sample of data. I always get the error about expecting [2,1] dimensions for the data I want to predict too. I would aspect I train using more samples and I can predict on other bunch of data which is given in the form of 1sample (same number of features, same number of time steps), so:

training data : (2,2000,1)

validation data : (1,2000,1)

prediction data : (1,2000,1)

I see 2000 time steps may be too long, but even if I reduce time steps, the data shape for prediction is not accepted by the predict() function.

Thanks

Not sure about why you’re getting an error – but the shape of your data does not look right. Too many time steps and far too few samples.