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catch22 Python Usage Examples

catch22 is a subset of the 22 best-performing time-series features, distilled from a comprehensive library of over 7000 time-series features in hctsa. The purpose of this repository is to guide new users through the practical utilisation of catch22 within their Python-based time-series analysis workflows.

To get started, you'll need to install the latest version of pycatch22 via pip:

pip install pycatch22

📚 Notebook Descriptions

The catch22 usage examples are organised as individual Jupyter notebooks, each focusing on a different application.

• catch22_pandas_example.ipynb: This notebook serves as an example for implementing catch22 and constructing a time-series $\times$ feature matrix in pandas. Here, you will learn how to extract time-series features from a general dataset and integrate them into a pandas data frame, providing a well-organised and accessible format for further time-series analysis.

📖 catch22 Feature Descriptions

For a more in-depth understanding of the time-series features comprising catch22, a clear and intuitive explanation of each feature can be found in the catch22 GitBook, including relevant examples and plots. A high-level summary of the time-series feature names, conceptual groupings, and brief descriptions are also provided in a feature overview table.

💻 Additional Implementations

While this repository specifically focuses on the Python (pycatch22) implementation, catch22 is available in multiple languages. Further details about the Julia (Catch22.jl), R (Rcatch22) and MATLAB implementations of catch22 can be found in the original repository and wiki.

📌 Resources

• catch22 - CAnonical Time-series CHaracteristics
• Lubba et al. (2019). catch22: CAnonical Time-series CHaracteristics.

Todo:

• Add a more general time-series dataset format (i.e. csv format).