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aeon

Classify, regress, cluster, segment, and transform time series data with a scikit-learn-compatible API.

298|27|Updated Feb 18, 2026
Search Tags:#machine learning#time series#regression#classification#feature extraction#segmentation#clustering
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill aeon-jaechang-hits
Or copy as Structured Prompt for Agent▌
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/scientific-computing/aeon
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill aeon-jaechang-hits

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a unified, scikit-learn-compatible API for a wide range of time series machine learning tasks, simplifying complex analyses.

Core Features & Use Cases

  • Classification: Classify time series using state-of-the-art algorithms like ROCKET and MiniROCKET.
  • Regression: Predict continuous targets from time series inputs.
  • Clustering: Group similar time series based on shape.
  • Segmentation: Detect change points and segment continuous time series.
  • Transformation: Extract fixed-length features from variable-length time series.
  • Use Case: Analyze sensor data from wearable devices to classify user activity (walking, running, resting).

Quick Start

Use the aeon skill to classify time series data 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 classify time series data using scikit-learn-compatible algorithms?▌

Time series classification can be performed using this toolkit's scikit-learn-compatible API with algorithms like ROCKET and InceptionTime to categorize univariate or multivariate sequential data.

What is the best way to cluster unequal-length time series data?▌

Clustering unequal-length time series is supported through shape-based grouping algorithms within this toolkit, allowing you to segment similar temporal patterns without requiring uniform data lengths.

Can I use scikit-learn pipelines for time series regression and feature extraction?▌

Scikit-learn pipelines are fully compatible for time series regression and feature extraction, enabling you to transform variable-length sequential inputs into fixed-length features and predict continuous targets.

Does this time series machine learning toolkit support change point detection and segmentation?▌

Change point detection and time series segmentation are natively supported, allowing you to identify structural shifts and split continuous sequential data into meaningful discrete segments.

How do I handle variable-length time series for machine learning tasks?▌

Variable-length time series are handled natively through built-in transformation algorithms that extract fixed-length features, making the data compatible with standard classification and regression workflows.

What algorithms are available for time series classification besides KNN-DTW?▌

Time series classification includes over 30 algorithms such as ROCKET, MiniROCKET, InceptionTime, HIVE-COTE, and WEASEL, covering both distance-based and feature-based approaches for diverse sequential data patterns.

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