Keras LSTM with 1D time series I'm learning how to use Keras and I've had reasonable success with my labelled dataset using the examples on Chollet's Deep Learning for Python. The data set is ~1000 Time Series with length 3125 with 3 potential classes.
I'd like to go beyond the basic Dense layers which give me about 70% prediction rate and the book goes on to discuss LSTM and RNN layers.
All the examples seem to use datasets with multiple features for each timeseries and I'm struggling to work out how to implement my data as a result.
If for example, I have 1000x3125 Time Series, how do I feed that into something like the SimpleRNN or LSTM layer? Am I missing some fundamental knowledge of what these layers do?
Current code:
import pandas as pd
import numpy as np
import os
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Dropout, SimpleRNN, Embedding, Reshape
from keras.utils import to_categorical
from keras import regularizers
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

def readData():
    # Get labels from the labels.txt file
    labels = pd.read_csv('labels.txt', header = None)
    labels = labels.values
    labels = labels-1
    print('One Hot Encoding Data...')
    labels = to_categorical(labels)

    data = pd.read_csv('ts.txt', header = None)

    return data, labels

print('Reading data...')
data, labels = readData()

print('Splitting Data')
data_train, data_test, labels_train, labels_test = train_test_split(data, labels)

print('Building Model...')
#Create model
model = Sequential()
## LSTM / RNN goes here ##
model.add(Dense(3, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 
print('Training NN...')
history = model.fit(data_train, labels_train, epochs=1000, batch_size=50,
    validation_split=0.25,verbose=2)

results = model.evaluate(data_test, labels_test)

predictions = model.predict(data_test)

print(predictions[0].shape)
print(np.sum(predictions[0]))
print(np.argmax(predictions[0]))

print(results)

acc = history.history['acc']
val_acc = history.history['val_acc']
epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
LSTM layers require data of a different shape.

From your description, I understand the starting dataset to have 3125 rows and 1000 columns, where each row is one time-step. The target variable should then have 3125 rows and 1 column, where each value can be one of three possible values. So it sounds like you're doing a classification problem. To check this in code, I would do:

&gt;&gt;&gt; X.shape
(3125, 1000)

&gt;&gt;&gt; y.shape
(1000,)


The LSTM class requires each single sample to consist of a 'block' of time. Let's say you want to have a block of 100 time-steps. This means X[0:100] is a single input sample, which corresponds to the target variable at y[100]. this means your window size (a.k.a number of time-steps or number of lags) is equal to 100. As stated above, you have 3125 samples, so N = 3125. To form the first block, we unfortunately have to discard the first 100 samples of y, as we cannot form an entire block of 100 from the available data (we would end up needing the data points before X[0]).

Given all this, an LSTM requires you to deliver batches of shape (N - window_size, window_size, num_features), which translates into (3125 - 100, 100, 1000) == (3025, 100, 1000).

Creating these time-blocks is a bit of a hassle, but create a good function once, then save it  :)

There is more work to be done, perhaps look at more in depth examples of my explanation above here... or have a read of the LSTM documentation, (or better still, the source code!).

The final model would then be simple enough (based on your code):

#Create model
model = Sequential()
model.add(LSTM(units=32, activation='relu',
               input_shape=(100, 1000))    # the batch size is neglected!
model.add(Dense(3, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam',
              metrics=['accuracy'])


Have a look at the documentation regarding input shape for the Sequential model. It basically says that we don't need to specify the number of batches within input_shape. It can be done with e.g. batch_size=50, if you require it to be a fixed number.

I know the input_shape argument is not in the documentation for LSTM, but the class itself inherits from RNN, which in turn inherits from Layer - so it will be able to use the info you provide.

One last tip: if you plan on adding several LSTM layers ('stacking' them), then you shall need to add one more argument to all but the last LSTM, namely, the return_sequences=True.