More machine learning methods for time-series

The algorithms that we'll cover in this section are all highly competitive for forecasting and prediction tasks. If you are looking for a discussion of state-of-the-art machine learning algorithms, please refer to Chapter 4, Introduction to Machine Learning for Time-Series.

In the aforementioned chapter, we've briefly discussed a few of these algorithms, but we'll discuss them here in more detail and we will also introduce other algorithms that we haven't discussed before, such as Silverkite, gradient boosting, and k-nearest neighbors.

We'll dedicate a separate practice section to a library that was released in 2021, which is facebook's Kats. Kats provides many advanced features, including hyperparameter tuning and ensemble learning. On top of these features, they implement feature extraction based on the TSFresh library and include many models, including Prophet, SARIMA, and others. They...