Forecasting Time Series with Machine Learning Models

In the previous chapter, we started looking at machine learning as a tool to solve the problem of time series forecasting. We talked about a few techniques such as time delay embedding and temporal embedding, both of which cast a time series forecasting problem as a classical regression problem from the machine learning paradigm. In this chapter, we’ll look at these techniques in detail and go through them in a practical sense using the London Smart Meters dataset we have been working with throughout this book.

In this chapter, we will cover the following topics:

• Training and predicting with machine learning models
• Generating single-step forecast baselines
• Standardized code to train and evaluate machine learning models
• Training and predicting for multiple households