Machine Learning

How to Use the TimeseriesGenerator for Time Series Forecasting in Keras

Last Updated on August 28, 2020

Time series data must be transformed into a structure of samples with input and output components before it can be used to fit a supervised learning model.

This can be challenging if you have to perform this transformation manually. The Keras deep learning library provides the TimeseriesGenerator to automatically transform both univariate and multivariate time series data into samples, ready to train deep learning models.

In this tutorial, you will discover how to use the Keras TimeseriesGenerator for preparing time series data for modeling with deep learning methods.

After completing this tutorial, you will know:

  • How to define the TimeseriesGenerator generator and use it to fit deep learning models.
  • How to prepare a generator for univariate time series and fit MLP and LSTM models.
  • How to prepare a generator for multivariate time series and fit an LSTM model.

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.

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