Technical requirements

Throughout this chapter, you will be using the same datasets and functions used in Chapter 12, Forecasting Using Supervised Machine Learning. The handle_missing_data and one_step_forecast functions will remain the same.

The Standardize class will be modified slightly to include a split_data method that splits a dataset into train, validation, and test sets. The validation set is used to evaluate the model's performance at each epoch. The following is the updated code for the Standardize class that you will be using throughout this chapter:

1. Start by loading the datasets and preprocessing the time series to be suitable for supervised learning. These are the same steps you followed in Chapter 12, Forecasting Using Supervised Machine Learning:

   Class Standardize:
       def __init__(self, df, split=0.10):
           self.data = df
           self.split = split...