Machine Learning

How to Develop LSTM Models for Time Series Forecasting

Last Updated on August 28, 2020

Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting.

There are many types of LSTM models that can be used for each specific type of time series forecasting problem.

In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.

The objective of this tutorial is to provide standalone examples of each model on each type of time series problem as a template that you can copy and adapt for your specific time series forecasting problem.

After completing this tutorial, you will know:

  • How to develop LSTM models for univariate time series forecasting.
  • How to develop LSTM models for multivariate time series forecasting.
  • How to develop LSTM models for multi-step time series forecasting.

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