How to Use Features in LSTM Networks for Time Series Forecasting

Last Updated on August 28, 2020

The Long Short-Term Memory (LSTM) network in Keras supports multiple input features.

This raises the question as to whether lag observations for a univariate time series can be used as features for an LSTM and whether or not this improves forecast performance.

In this tutorial, we will investigate the use of lag observations as features in LSTM models in Python.

After completing this tutorial, you will know:

• How to develop a test harness to systematically evaluate LSTM features for time series forecasting.
• The impact of using a varied number of lagged observations as input features for LSTM models.
• The impact of using a varied number of lagged observations and matching numbers of neurons for LSTM models.

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