Aeon

When you need to classify time series, detect anomalies, or forecast temporal data, this gives you scikit-learn compatible access to the aeon toolkit. It covers the full range: classification, regression, clustering, forecasting, segmentation, and similarity search with algorithms like ROCKET, DTW, and Inception networks. The docs are thorough about version differences (targets aeon 1.x, which reworked forecasting from 0.x) and honestly flag experimental modules. Particularly useful if you're dealing with sensor data, financial sequences, or any pattern recognition over time and want proven algorithms without rolling your own temporal feature engineering. Needs Python 3.10+ and explicit installation of aeon extras for deep learning models.

skills.sh installs: 2.1K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill aeon --agent claude-code

Installs into .claude/skills of the current project.

Files

Aeon Time Series Machine Learning

Overview

Aeon is a scikit-learn compatible Python toolkit for time series machine learning (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.