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aeon

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

2|Updated Jun 4, 2026
Search Tags:#machine learning#time series#forecasting#classification#anomaly detection#clustering
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill aeon-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/aeon
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill aeon-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires aeon, scikit-learn, numpy, matplotlib, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of analyzing temporal data by providing a unified, scikit-learn compatible interface for diverse time series tasks, eliminating the need to switch between disparate libraries for classification, forecasting, and anomaly detection.

Core Features & Use Cases

  • Comprehensive Analysis: Perform classification, regression, clustering, forecasting, anomaly detection, and segmentation within a single ecosystem.
  • Advanced Algorithms: Access state-of-the-art methods like ROCKET for feature extraction, InceptionTime for deep learning, and Matrix Profile for motif discovery.
  • Use Case: A researcher can use this skill to first segment sensor data into distinct activity regimes, then classify those regimes using a ROCKET-based pipeline, and finally forecast future sensor trends using an ARIMA model.

Quick Start

Use the aeon skill to classify the provided time series dataset using the RocketClassifier algorithm.

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 single scikit-learn workflow?▼

Time series classification and forecasting can be unified in a scikit-learn compatible workflow by using the aeon toolkit, which provides compatible APIs for univariate and multivariate temporal data analysis across diverse tasks.

What algorithms are available for time series feature extraction and deep learning?▼

Available algorithms for time series feature extraction and deep learning include ROCKET for feature extraction, InceptionTime for deep learning architectures, and Matrix Profile for motif discovery within the aeon ecosystem.

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

Yes, you can use scikit-learn pipelines for multivariate time series anomaly detection because the aeon library provides compatible APIs that support both univariate and multivariate temporal data analysis.

Does the aeon toolkit support time series clustering and segmentation?▼

Yes, the aeon toolkit supports time series clustering and segmentation, providing a comprehensive suite of algorithms for classification, regression, clustering, forecasting, anomaly detection, and segmentation.

What is the best way to analyze temporal data without switching between multiple libraries?▼

The best way to analyze temporal data without switching libraries is using the aeon toolkit, which provides a unified interface for diverse time series tasks using specialized temporal distance metrics and deep learning architectures.

Do I need numpy and matplotlib installed to execute time series machine learning algorithms?▼

Yes, you need numpy and matplotlib installed as dependencies, along with aeon and scikit-learn, to execute the specialized time series machine learning algorithms and visualize temporal data results.

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