How Do Time Steps and Features Work in Keras LSTMs?

Understanding LSTM Time Steps and Features

In the given Keras code, the trainX array has a shape of (samples, time steps, features). This means that the data is reshaped into a three-dimensional array, where the first dimension represents the number of samples, the second dimension represents the number of time steps, and the third dimension represents the number of features.

In the context of the diagram you provided, each green box represents a time step, and each pink box represents a feature. Therefore, the code is considering the "many to one" case, where there are multiple pink boxes (features) for each green box (time step).

The features argument becomes relevant when considering multivariate series, such as modeling two financial stocks simultaneously. In this case, each feature would represent one of the stocks, and the number of features would be equal to the number of stocks being modeled.

Understanding Stateful LSTMs

Stateful LSTMs do not save the cell memory values between runs of batches. Instead, they maintain their state internally across batches. In the case of the provided code, where the batch size is one, the LSTM will update its internal state based on the current input and use that state to process the next input. By resetting the states between training runs, the LSTM is forced to start fresh with each batch.

This behavior is important for cases such as predicting future time steps, where the model needs to remember the context of the past inputs to make accurate predictions.

Additional Notes

• The term "time steps" refers to the number of sequential elements in a sequence. In the context of LSTMs, time steps are typically thought of as representing time intervals.
• The term "features" refers to the different types of data being used in a model. For example, in the case of a financial stock model, the features could be the stock's open, close, high, and low prices.

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