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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. One of the time series featurization libraries I've liked but haven't used in anger is catch22:
- https://github.com/chlubba/catch22
- https://link.springer.com/article/10.1007/s10618-019-00647-x
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...
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