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LeonChaoX
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aeon

Build scikit-learn compatible time-series models for classification, regression, clustering, forecasting, and anomaly detection.

783|65|Updated Feb 27, 2026
Search Tags:#time series#forecasting#classification#feature extraction#anomaly detection#similarity search#clustering
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill aeon-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/09-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%8E%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/aeon
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill aeon-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you train accurate machine learning solutions for time series by providing a scikit-learn compatible toolbox for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Core Features & Use Cases

  • Time series modeling across tasks: Use aeon for temporal classification, continuous prediction (regression), unsupervised grouping (clustering), future value prediction (forecasting), and subsequence/point anomaly detection.
  • Temporal pattern discovery & search: Detect motifs/discords and perform similarity search using matrix-profile based methods and specialized distance metrics.
  • Feature extraction for stronger baselines: Generate ROCKET/Catch22 features to feed into traditional sklearn models for robust performance on many datasets.
  • Use Case: You have sensor readings indexed by time and want to (1) forecast the next horizon, (2) flag unusual events, and (3) cluster recurring behavior patterns into groups—all within one consistent API style.

Quick Start

Ask the AI to train a time-series classifier using a ROCKET variant on a benchmark dataset like GunPoint, then report the test accuracy.

Frequently Asked Questions about aeon

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

FAQPage Schema
How do I build machine learning models for time series classification and forecasting?▼

You can build machine learning models for time series classification and forecasting by applying scikit-learn compatible algorithms that provide task-specific estimators, specialized distance metrics, and feature extraction pipelines tailored to temporal dependencies.

Can I use scikit-learn pipelines for time series anomaly detection and segmentation?▼

Yes, you can use scikit-learn compatible pipelines for time series anomaly detection and segmentation, applying specialized algorithms designed to flag unusual events and split temporal sequences into meaningful segments.

How do I extract features from temporal data to improve clustering performance?▼

Extract features from temporal data for clustering by generating ROCKET or Catch22 features, which transform time series shapes into robust baseline inputs for traditional machine learning models to improve grouping accuracy.

What is the best way to perform similarity search on univariate and multivariate temporal sequences?▼

The best way to perform similarity search on univariate and multivariate temporal sequences is using matrix-profile based methods combined with specialized distance metrics to detect recurring motifs and discords.

Do I need specialized distance metrics for time series regression tasks?▼

Yes, specialized distance metrics are needed for time series regression tasks because they account for temporal dependencies and sequence shapes, enabling more accurate continuous value prediction compared to standard metrics.

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