Building, Training, and Deploying an LSTM-Based RNN

Let's proceed with the next step: building a simple LSTM-based RNN for demand prediction. First, we will train the network, then we will test it, and finally, we will deploy it. In this case study, we used no validation set for the network and we performed no optimization on the static hyperparameters of the network, such as, for example, the size of the LSTM layer.

A relatively simple network is already achieving good error measures on the test set for our demand prediction task, and therefore, we decided to focus this section on how to test a model for time series prediction rather than on how to optimize the static parameters of a neural network. We looked at the optimization loop in Chapter 5, Autoencoder for Fraud Detection. In general, this optimization loop can also be applied to optimize network hyperparameters. Let's begin by building an LSTM-based RNN.

Building the LSTM-Based RNN

For this case study, we...