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catch22 Features
  • Overview
  • Feature overview table
  • catch22
    • Distribution shape
    • Extreme event timing
    • Linear autocorrelation structure
    • Nonlinear autocorrelation
    • Symbolic
    • Incremental differences
    • Simple forecasting
    • Self-affine scaling
    • Other
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Overview

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Last updated 1 year ago

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THIS WEBSITE IS NO LONGER MAINTAINED; IT HAS BEEN REPLACED BY A NEW, UPDATED VERSION THAT IS ACTIVELY MANTAINED, AVAILABLE AT

Please check out all of these resources, and more over there!

Want to do feature-based time-series analysis, fast, and in a coding language of your choice? The catch22 feature set provides open access to a powerful reduced set of time-series analysis features (extracted from the full hctsa feature set) that compute quickly. But the features have long scary names that can be difficult to interpret…

Welcome to a collection of catch22 feature descriptions!

In this document, we aim to explain the catch22 features in a clear and intuitive way, with plots and examples.

Being written in the beautiful GitBook platform, you can comment into the document if you have any questions or spot any errors.

Happy exploring!

Links:

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catch22 GitHub page
catch22 wiki.
hctsa GitHub page
https://time-series-features.gitbook.io/catch22
The catch22 feature set is a fast, C-coded set of statistical time-series features.