Summary
To round up the second part of the book nicely, we explored GFMs in detail and saw why they are important and why they are an exciting new direction in time series forecasting. We saw how we can use a GFM using machine learning models and also reviewed many techniques to make GFMs perform better, most of which are quite frequently used in competitions and industry use cases alike. Now that we have wrapped up the machine learning section of the book, we will move on to a specific type of machine learning that has become well-known over the past few years – deep learning – in the next chapter.
