Kats
catch22
| Kats | catch22 | |
|---|---|---|
| 79 | 2 | |
| 4,761 | 319 | |
| 0.6% | 0.3% | |
| 8.0 | 5.8 | |
| 1 day ago | 6 days ago | |
| Python | C | |
| MIT License | GNU General Public License v3.0 only |
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Kats
catch22
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Kats: One stop shop for time series analysis in Python
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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How to statistically test whether two time-series are "different"?
Check out package called catch22, I believe this may be precisely what you need.
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
neural_prophet - NeuralProphet: A simple forecasting package
sktime - A unified framework for machine learning with time series
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
datascience - Curated list of Python resources for data science.
tablespoon - 🥄✨Time-series Benchmark methods that are Simple and Probabilistic