skills.rest
youyinnn
youyinnnCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

aeon

Provide a unified Python toolkit for time series machine learning tasks.

1|1|Updated Mar 13, 2026
Search Tags:#machine learning#deep learning#time series#forecasting#classification#anomaly detection
One-click install
npx skills add https://github.com/youyinnn/skills-collection --skill aeon-youyinnn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/youyinnn/skills-collection/tree/main/plugins/model-development-and-experiments/skills/aeon
Command: npx skills add https://github.com/youyinnn/skills-collection --skill aeon-youyinnn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires aeon, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to tackle complex time series analysis tasks, from forecasting and anomaly detection to classification and clustering, by providing access to a comprehensive suite of state-of-the-art algorithms.

Core Features & Use Cases

  • Diverse Algorithms: Offers a wide range of methods including deep learning, statistical models, and feature-based approaches.
  • Task Versatility: Supports classification, regression, clustering, forecasting, anomaly detection, and segmentation.
  • Use Case: A data scientist can use this Skill to build a robust model for predicting future stock prices, identifying unusual trading patterns, and classifying different market regimes.

Quick Start

Use the aeon skill to classify the time series data in 'sensor_readings.csv' using the RocketClassifier.

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 on sensor data?▼

Time series anomaly detection identifies unusual patterns in sequential data using statistical models and deep learning architectures. This toolkit applies these algorithms to your temporal data to flag significant deviations from expected behavior.

What's the best way to forecast future values from temporal data?▼

Yes, you can perform time series clustering to group similar temporal sequences together. The toolkit provides specialized algorithms that segment and cluster time series data, allowing you to identify shared patterns across multiple temporal datasets.

Do I need to install the aeon library to use this toolkit?▼

Deep learning architectures for time series analysis are integrated directly within this toolkit. They function alongside statistical models and feature-based methods, all accessible through scikit-learn compatible APIs for comprehensive temporal data analysis.

Can I use scikit-learn APIs for time series regression tasks?▼

Yes, time series regression tasks utilize scikit-learn compatible APIs. This unified Python toolkit integrates advanced deep learning and statistical methods, allowing you to seamlessly apply regression models to your temporal data analysis workflow.

Related Skills

Explore More Data & Analytics Skills→
Lord1EgyptLord1Egypt
2

aeon

Classify, forecast, and cluster time series with scikit-learn compatible APIs.

Community
Advanced
vadimcomanescuvadimcomanescu
24

aeon

Train ROCKET-based classifiers on time series datasets with scikit-learn compatible APIs.

Community
Advanced
pur3v4d3rpur3v4d3r
6

scikit-learn

Train and evaluate classical machine learning models with scikit-learn.

Community
Advanced
Weiwei-MaoWeiwei-Mao
2

aeon

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

Community
Advanced