[acidbaseextract]

The time series feature (TSFeature) extraction module in Kats can produce 65 features with clear statistical definitions, which can be incorporated in most machine learning (ML) models...

I'd be curious about the performance of these. A time series featurization library I've liked the look of but haven't used for real is catch22: https://github.com/chlubba/catch22

In particular I like catch22's methodology:

catch22 is a collection of 22 time-series [that are] are a high-performing subset of the over 7000 features in hctsa. Features were selected based on their classification performance across a collection of 93 real-world time-series classification problems...

[StreakyCobra]

There is also "tsfresh" [1] in the same domain that does «Automatic extraction of 100s of features». It filters the most useful features according to the given task, I quote: «This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.»

[1]: https://github.com/blue-yonder/tsfresh