How to Update LSTM Networks During Training for Time Series Forecasting

Last Updated on September 6, 2020

A benefit of using neural network models for time series forecasting is that the weights can be updated as new data becomes available.

In this tutorial, you will discover how you can update a Long Short-Term Memory (LSTM) recurrent neural network with new data for time series forecasting.

After completing this tutorial, you will know:

• How to update an LSTM neural network with new data.
• How to develop a test harness to evaluate different update schemes.
• How to interpret the results from updating LSTM networks with new data.

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/2017: Added the missing update_model() function.
• Updated Apr/2019: Updated the link to dataset.

Tutorial Overview

This tutorial is divided into 9 parts. They are:

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