How to Seed State for LSTMs for Time Series Forecasting in Python

Last Updated on August 28, 2020

Long Short-Term Memory networks, or LSTMs, are a powerful type of recurrent neural network capable of learning long sequences of observations.

A promise of LSTMs is that they may be effective at time series forecasting, although the method is known to be difficult to configure and use for these purposes.

A key feature of LSTMs is that they maintain an internal state that can aid in the forecasting. This raises the question of how best to seed the state of a fit LSTM model prior to making a forecast.

In this tutorial, you will discover how to design, execute, and interpret the results from an experiment to explore whether it is better to seed the state of a fit LSTM from the training dataset or to use no prior state.

After completing this tutorial, you will know:

• About the open question of how to best initialize the state of a fit LSTM for forecasting.
• How to develop a robust test harness for evaluating LSTM models on univariate time series forecasting problems.
• How to determine whether or not seeding the state of your LSTM prior to forecasting is a good idea on your
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