aeon

Name: aeon
Availability: InStock
Author: K-Dense

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

Ready to run

npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill aeon-k-dense-ai

💬 Prompt for ChatGPT / Claude Chat▼

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.