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aeon

Solve time-series machine learning tasks with a scikit-learn-compatible toolkit.

21|1|Updated Mar 19, 2026
Search Tags:#forecasting#time-series#classification#machine-learning#feature-extraction#aeon-toolkit
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill aeon-ownlabai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/aeon
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill aeon-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Aeon provides a comprehensive, scikit-learn-compatible toolkit for time-series machine learning, enabling practitioners to apply a broad range of algorithms to temporal data without leaving familiar scikit-learn workflows.

Core Features & Use Cases

  • Time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a unified API.
  • Feature extraction and transformation through ROCKET, Catch22, tsfresh, SFA, SAX, and deep architectures, plus ensemble and hybrid methods.
  • Use cases across finance, healthcare, IoT sensor data, and engineering for rapid prototyping, benchmarking, and production-grade pipelines.

Quick Start

Run a quick example pipeline using ROCKET features and a classifier to classify a time series.

Frequently Asked Questions about aeon

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

FAQPage Schema
How do I perform time-series classification using scikit-learn-compatible APIs?▼

Time-series classification can be performed using a unified, scikit-learn-compatible API. This toolkit enables practitioners to apply a broad range of algorithms to temporal data without leaving familiar scikit-learn workflows for rapid prototyping and benchmarking.

Can I use ROCKET and Catch22 feature extraction for time-series forecasting?▼

Yes, time-series forecasting and feature extraction are supported through ROCKET, Catch22, tsfresh, SFA, and SAX. These transformations provide consistent APIs to process temporal data for downstream machine learning pipelines.

What time-series machine learning tasks are supported beyond forecasting?▼

Time-series machine learning tasks supported include regression, clustering, anomaly detection, segmentation, and similarity search. The toolkit provides broad algorithm coverage with modular components for end-to-end temporal data workflows.

Is there a scikit-learn alternative for anomaly detection on IoT sensor data?▼

This toolkit offers scikit-learn-compatible anomaly detection for IoT sensor data and other temporal data. It provides consistent APIs and modular components to build production-grade pipelines without leaving familiar workflows.

Do I need deep learning frameworks to handle time-series segmentation?▼

No, deep learning integration is available but not required for time-series segmentation. The toolkit supports ensemble, hybrid methods, and feature-based approaches to handle segmentation tasks across diverse temporal datasets.

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