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aeon

Run machine learning algorithms for time series analysis with the aeon package.

Updated May 17, 2026
Search Tags:#machine learning#time series#regression#forecasting#classification#anomaly detection#segmentation
One-click install
npx skills add https://github.com/galeep/plugin-place --skill aeon-galeep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-machine-learning/skills/aeon
Command: npx skills add https://github.com/galeep/plugin-place --skill aeon-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses complex time series machine learning tasks by providing a comprehensive toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Core Features & Use Cases

  • Time Series Classification: Categorize time series into predefined classes, suitable for univariate and multivariate time series analysis.
  • Time Series Regression: Predict continuous values from time series data, ideal for forecasting future values.
  • Time Series Clustering: Group similar time series without labels, useful for pattern discovery.
  • Forecasting: Predict future time series values, includes deep learning and statistical models.
  • Anomaly Detection: Identify unusual patterns or outliers in time series data.
  • Segmentation: Partition time series into regions with change points, useful for regime change detection.
  • Similarity Search: Find similar patterns within or across time series, including subsequence search and motif discovery.
  • Use Case: Imagine you have a dataset of stock prices and you want to predict future prices. Use this Skill to apply various forecasting models and evaluate their performance.

Quick Start

Use the aeon skill to train a time series classification model on the provided dataset 'stock_prices.csv'.

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 Python?▼

You can perform time series classification and forecasting using this toolkit, which provides scikit-learn compatible APIs and deep learning models optimized for univariate and multivariate time series data.

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

To detect anomalies in multivariate time series data, this toolkit provides specialized algorithms to identify unusual patterns and outliers across your dataset without requiring explicit labels.

Can I use scikit-learn compatible APIs for time series regression and clustering?▼

Yes, you can use scikit-learn compatible APIs for time series regression and clustering, allowing you to predict continuous values and group similar univariate or multivariate time series.

Do I need Python 3.10 or higher to run time series machine learning models?▼

Yes, you need Python 3.10 or higher along with the aeon package installed to run these time series machine learning models for segmentation, similarity search, and forecasting.

How does time series segmentation and similarity search work for regime change detection?▼

Time series segmentation partitions data into regions with change points for regime change detection, while similarity search finds matching patterns or subsequences within and across time series.

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