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aeon

Integrate end-to-end time-series machine learning workflows with scikit-learn-style APIs.

14|2|Updated Feb 24, 2026
Search Tags:#workflow#evaluation#forecasting#time-series#preprocessing#machine-learning
One-click install
npx skills add https://github.com/andikarachman/data-science-plugin --skill aeon-andikarachman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/andikarachman/data-science-plugin/tree/main/skills/aeon
Command: npx skills add https://github.com/andikarachman/data-science-plugin --skill aeon-andikarachman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Aeon provides a comprehensive toolkit for time series machine learning, enabling end-to-end workflows from preprocessing to evaluation and deployment, reducing the friction of building time-series models.

Core Features & Use Cases

  • Time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search across temporal data.
  • Feature extraction and transformations (Catch22, ROCKET features, tsfresh) to enable traditional ML pipelines.
  • Experimentation, evaluation, and deployment patterns that align with standard DS workflows and institutional knowledge compounding.

Quick Start

Install aeon and run a basic time-series classification pipeline to get started.

Frequently Asked Questions about aeon

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an end-to-end time-series machine learning workflow?▼

Build an end-to-end time-series machine learning workflow by using a toolkit that handles preprocessing, feature extraction, model training, and evaluation, exposing scikit-learn–style APIs for rapid prototyping and deployment.

What time-series analysis tasks are covered by standard machine learning pipelines?▼

Standard time-series machine learning pipelines cover classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search across temporal data.

Can I use scikit-learn APIs for time-series forecasting and classification?▼

Yes, you can use scikit-learn–style APIs for time-series forecasting and classification, ensuring seamless integration with existing scientific Python libraries and traditional ML pipelines.

Do I need 3D data input shape for time-series anomaly detection?▼

Yes, time-series anomaly detection requires a 3D data input shape to properly process temporal sequences, alongside standard Python scientific libraries for execution.

What is the best way to extract features like Catch22 or ROCKET for time-series classification?▼

Extract features like Catch22 or ROCKET for time-series classification by applying built-in transformations that enable traditional ML pipelines to process temporal data efficiently.

Are there limitations when integrating time-series transformations with traditional ML pipelines?▼

Limitations when integrating time-series transformations with traditional ML pipelines include strict 3D data input shape requirements and dependency on standard scientific Python libraries for execution.

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