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aeon

Automate time series modeling workflows with a scikit-learn compatible toolkit.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#regression#forecasting#time-series#classification#clustering#anomaly-detection#aeon
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill aeon-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/aeon
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill aeon-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Aeon simplifies building, validating, and deploying time series models by providing a unified, scikit-learn compatible toolkit with diverse algorithms and utilities.

Core Features & Use Cases

  • Time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a wide range of ready-to-use algorithms.
  • Supports feature extraction, transformation, and benchmarking to accelerate scientific experiments and research workflows.
  • Integrates with Python data science stack and provides references for deeper exploration of methods.

Quick Start

Install the aeon package and try a quick example to train a simple time series classifier on a familiar dataset.

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 and forecasting in Python?▼

You can perform time series classification and forecasting using a scikit-learn compatible toolkit that provides modular algorithms for building and validating models on temporal data.

What is the best way to run anomaly detection on time series data?▼

For time series anomaly detection, a unified toolkit offers ready-to-use algorithms that integrate directly with the Python data science stack to identify outliers in temporal data.

Can I use scikit-learn workflows for time series clustering and regression?▼

Yes, this time series toolkit is fully scikit-learn compatible, allowing you to apply familiar workflows for clustering, regression, and similarity search on temporal data.

Does this time series toolkit support feature extraction and benchmarking?▼

Yes, the toolkit supports feature extraction, transformation, and benchmarking to accelerate scientific experiments and research workflows across various time series tasks.

What tasks can I accomplish with a unified time series machine learning toolkit?▼

A unified time series toolkit enables classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a wide range of algorithms.

Do I need additional dependencies to run time series segmentation with this toolkit?▼

No additional dependencies are required beyond the Python package itself, which provides modular references for deeper exploration and integrates natively with existing data science environments.

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