Machine Learning

Stateful and Stateless LSTM for Time Series Forecasting with Python

Last Updated on August 28, 2020

The Keras Python deep learning library supports both stateful and stateless Long Short-Term Memory (LSTM) networks.

When using stateful LSTM networks, we have fine-grained control over when the internal state of the LSTM network is reset. Therefore, it is important to understand different ways of managing this internal state when fitting and making predictions with LSTM networks affect the skill of the network.

In this tutorial, you will explore the performance of stateful and stateless LSTM networks in Keras for time series forecasting.

After completing this tutorial, you will know:

  • How to compare and contrast stateful and stateless LSTM networks for time series forecasts.
  • How the batch size in stateless LSTMs relate to stateful LSTM networks.
  • How to evaluate and compare different state resetting regimes for stateful LSTM networks.

Kick-start your project with my new book Deep Learning for Time Series Forecasting, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

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