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aeon

Train time series classifiers, regressors, forecasters, and clusterers via scikit-learn compatible APIs.

2|Updated Feb 2, 2026
Search Tags:#machine learning#time series#forecasting#classification#anomaly detection#clustering
One-click install
npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill aeon-weiwei-mao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/aeon
Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill aeon-weiwei-mao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles complex time series analysis challenges, from forecasting and anomaly detection to classification and clustering, providing state-of-the-art machine learning tools.

Core Features & Use Cases

  • Time Series Classification/Regression: Categorize or predict values from sequential data.
  • Forecasting: Predict future values of time series.
  • Anomaly Detection: Identify unusual patterns or outliers.
  • Clustering: Group similar time series.
  • Use Case: Predict future energy demand based on historical consumption patterns, or classify different types of sensor readings to detect equipment malfunctions.

Quick Start

Use the aeon skill to train a RocketClassifier on the GunPoint dataset and evaluate its accuracy.

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 forecasting with machine learning?▼

Time series forecasting predicts future values from historical sequential data. This toolkit provides advanced deep learning architectures and scikit-learn compatible APIs to train forecasting models directly on temporal data.

Can I use scikit-learn compatible APIs for time series classification?▼

Yes, you can use scikit-learn compatible APIs for time series classification. This toolkit supports categorizing sequential data, like classifying sensor readings to detect equipment malfunctions, using standardized machine learning interfaces.

What is the best way to detect anomalies in multivariate time series?▼

Anomaly detection in multivariate time series identifies unusual patterns or outliers by leveraging specialized distance metric computations and advanced deep learning architectures. This approach isolates deviations from expected sequential behaviors across multiple variables.

Does this time series toolkit support both univariate and multivariate series?▼

Yes, this time series toolkit supports both univariate and multivariate series. It enables feature extraction, transformation, and specialized distance metric computations for temporal data analysis across varying dimensional complexities.

How do I cluster similar time series data?▼

Clustering similar time series data involves grouping sequential records by computing specialized distance metrics for temporal data analysis. This toolkit provides machine learning tools to extract features and identify similar patterns across multiple sequences.

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