Forecasting with a GRU using Keras

The GRU was proposed as an alternative to the RNN to combat the vanishing gradient problem by introducing the gates concept. As with an LSTM, the gates are used to regulate what and how the data flows. These gates are mathematical functions that act as filters to ensure only significant pieces of information are being retained.

How to do it...

In this recipe, you will continue from the Forecasting with an RNN using Keras recipe. All the time series preprocessing steps and the functions will be the same. The following steps will highlight any necessary changes needed. The energy consumption data is used in the following steps. The Jupyter notebook will include the steps and outputs for other datasets – air passengers and daily temperature:

1. Create another create_model function that is similar to the one you used in the previous recipe. The only difference will involve replacing the RNN layer with the GRU layer. You will use the...