Feature Engineering for Time Series Forecasting
In the previous chapter, we started looking at machine learning (ML) as a tool to solve the problem of time series forecasting. We also talked about a few techniques such as time delay embedding and temporal embedding, which cast time series forecasting problems as classical regression problems from the ML paradigm. In this chapter, we’ll look at those techniques in detail and go through them in a practical sense using the dataset we have been working with throughout this book.
In this chapter, we will cover the following topics:
- Feature engineering
- Avoiding data leakage
- Setting a forecast horizon
- Time delay embedding
- Temporal embedding
