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aeon

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

skills/aeon/SKILL.md

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Nov 10, 2026

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Nov 9, 2026
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MIT

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# Aeon Time Series Machine Learning

## Overview

Aeon is a scikit-learn compatible Python toolkit for time series machine learning ([aeon-toolkit.org](https://www.aeon-toolkit.org/)). It provides algorithms across classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, distances, transformations, benchmarking, and visualization — with a consistent estimator API.

**Version note:** Reviewed against **aeon 1.6.0** (Python 3.13). Small synthetic checks cover classification, regression, clustering, forecasts, preprocessing, distances, search, segmentation, matrix profiles, metrics, and local dataset I/O. Remote archive and TensorFlow training snippets are illustrative; they were not executed during this review. Reference catalogs are selected methods, not exhaustive lists. See the [1.6 release notes](https://www.aeon-toolkit.org/en/stable/changelogs/v1.6.html).

## When to Use This Skill

Apply this skill when:
- Classifying or predicting from time series data
- Detecting anomalies or change points in temporal sequences
- Clustering similar time series patterns
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