Feature engineering

Feature engineering, as the name suggests, is the process of engineering features from the data, mostly using domain knowledge, to make the learning process smoother and more efficient. In a typical ML setting, engineering good features is essential to get good performance from any ML model. Feature engineering is a highly subjective part of ML where each problem at hand has a different path – one that is hand-crafted to that problem.

When we are casting a time series problem as a regression problem, there are a few standard techniques that we can apply. This is a key step in the process because how well an ML model acquires an understanding of time is dependent on how well we engineer features to capture time. The baseline methods we covered in Chapter 4, Setting a Strong Baseline Forecast, are the methods that are created for the specific use case of time series forecasting and because of that, the temporal aspect of the problem is built into those...