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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 that compute quickly.
- A generally applicable subset of 22 features from the hctsa time-series feature library that provide an effective general summary of time-series statistical properties.
- Available as native Python, Julia, MATLAB, and R packages (that call C-compiled executables).
{% hint style="info" %} catch22 v1.0: Every feature now exactly reproduces a feature of hctsa v3.0.0, the final MATLAB release of hctsa. This changes the values of most features (and the names of several) relative to v0.x, so results are not comparable across versions. See the feature descriptions for what changed. The R and Julia packages do not yet implement v1.0. {% endhint %}
Choose your coding language below (or from the sidebar). Each pages cover all aspects of usage in a particular language including installation guides, tutorials and frequently asked questions.
| Python | python_light.png | python.md | ||
| MATLAB | MATLAB_light.png | matlab.md | ||
| R | R_light (1).png | r.md | ||
| Julia | julia_light.png | julia.md | ||
| C-compiled executable | C_light (1).png | c-compiled.md |
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Want to know what each time-series feature in the catch22 set computes? Access a high level summary of catch22 features with the feature table or delve into the specifics with detailed descriptions and tutorials on each feature.
| Summary Table | table_of_spis_light.png | feature-overview-table.md | ||
| Detailed Descriptions | got_light.png | feature-descriptions |
An ever-growing list of publications that use catch22.
| List of Publications | spi_descriptions_light.png | publications-using-catch22.md |
