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aeon

Analyze time-series data for classification, regression, forecasting, and anomaly detection.

1|Updated Mar 19, 2026
Search Tags:#forecasting#time-series#classification#clustering#machine-learning#aeon#scikit-learn
One-click install
npx skills add https://github.com/JosephWoodall/noosphere --skill aeon-josephwoodall
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/aeon
Command: npx skills add https://github.com/JosephWoodall/noosphere --skill aeon-josephwoodall

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 rapid development, evaluation, and deployment of models across classification, regression, forecasting, clustering, anomaly detection, segmentation, and similarity search.

Core Features & Use Cases

  • Unified API for classification, regression, forecasting, clustering, anomaly detection, segmentation, and similarity search on time-series data.
  • Rich collection and series transformers for feature extraction, preprocessing, and end-to-end pipelines.
  • Supports a broad ecosystem of algorithms (e.g., ROCKET-based features, Catch22, TSFresh) and deep learning networks within a single framework.

Quick Start

Install Aeon and run a ROCKET-based classifier on a sample time-series dataset to observe rapid results.

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 a scikit-learn workflow?▼

Time-series classification and forecasting can be performed using a unified, scikit-learn compatible API that provides modular transformers and algorithms for rapid model development and evaluation.

Can I use a single toolkit for both univariate and multivariate time-series anomaly detection?▼

Yes, you can apply anomaly detection, segmentation, and similarity search across both univariate and multivariate sequences using a single framework designed for diverse domains like finance and IoT.

What is the best way to extract features from time-series data for machine learning?▼

The best way to extract time-series features is using built-in series transformers that support algorithms like ROCKET, Catch22, and TSFresh within an end-to-end processing pipeline.

Does this time-series machine learning toolkit support deep learning networks?▼

Yes, the time-series machine learning toolkit supports a broad ecosystem of algorithms including deep learning networks alongside feature-based classifiers within its unified framework.

Are there limitations when applying time-series clustering to high-frequency IoT data?▼

While the toolkit handles extensive benchmarking and evaluation, limitations may arise from the computational complexity of clustering algorithms when applied to extremely high-frequency IoT sequences.

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