Machine Learning

Time Series Forecasting with the Long Short-Term Memory Network in Python

Last Updated on August 28, 2020

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations.

It seems a perfect match for time series forecasting, and in fact, it may be.

In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem.

After completing this tutorial, you will know:

  • How to develop a baseline of performance for a forecast problem.
  • How to design a robust test harness for one-step time series forecasting.
  • How to prepare data, develop, and evaluate an LSTM recurrent neural network for time series forecasting.

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.

  • Update May/2017: Fixed bug in invert_scale() function, thanks Max.
  • Updated Apr/2019: Updated the link to dataset.
Time Series Forecasting with the Long Short-Term Memory Network in Python

Time Series Forecasting with the Long Short-Term Memory Network in Python
Photo by Matt MacGillivray, some
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