Aeon API patterns for time series machine learning -- classification, regression, clustering, anomaly detection, segmentation, and similarity search. Use when /ds:experiment needs time-series-specific ML algorithms (ROCKET, InceptionTime, DTW classifiers), or /ds:eda needs temporal feature extraction (Catch22, ROCKET features) or change point detection. For classical statistical forecasting (ARIMA/SARIMAX) use statsmodels; for tabular ML pipelines use scikit-learn; for visualization use matplotlib.
How to install aeon?
andikarachman/data-science-plugin/aeon
$npx -y skills add andikarachman/data-science-plugin --skill aeonInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/andikarachman/data-science-plugin" --skill "andikarachman/data-science-plugin/aeon"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/andikarachman/data-science-plugin" that are relevant to the current task. Run `npx skills add "https://github.com/andikarachman/data-science-plugin"` and select the relevant skills, then follow their instructions.
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