This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two.
The easiest way to incorporate time series into your machine learning pipeline is to use them as features in a model. This chapter covers common features that are extracted from time series in order to do machine learning.
If you want to predict patterns from data over time, there are special considerations to take in how you choose and construct your model. This chapter covers how to gain insights into the data before fitting your model, as well as best-practices in using predictive modeling for time series data.
Once you've got a model for predicting time series data, you need to decide if it's a good or a bad model. This chapter coves the basics of generating predictions with models in order to validate them against "test" data.