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aeon

Train scikit-learn compatible time series classifiers, forecasters, and clusterers with aeon 1.x APIs.

74|5|Updated Dec 10, 2025
Search Tags:#machine learning#time series#forecasting#classification#anomaly detection#clustering#aeon
One-click install
npx skills add https://github.com/dralkh/seerai --skill aeon-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/dralkh/seerai/tree/main/skills/aeon
Command: npx skills add https://github.com/dralkh/seerai --skill aeon-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Aeon helps you analyze temporal data without stitching together ad hoc tooling. It gives you a unified way to classify, forecast, cluster, detect anomalies, segment change points, search for motifs, and extract features from time series.

Core Features & Use Cases

  • Time Series Classification: Train high-performing classifiers such as ROCKET, InceptionTime, shapelet-based models, and distance-based neighbors for labeled sequence data.
  • Forecasting and Regression: Predict future values or continuous targets with statistical forecasters, deep learning models, and feature-based regressors.
  • Anomaly Detection and Segmentation: Identify unusual points, subsequences, or regime changes in sensor streams, operational metrics, or scientific signals.
  • Similarity Search and Transformations: Find motifs, compute elastic distances, and convert raw series into informative features for downstream machine learning.
  • Use Case: A researcher can load a benchmark dataset, compare several classifiers, inspect published results, and choose the best model for a paper or experiment.

Quick Start

Use the aeon skill to load a time series dataset, train the most suitable estimator for your task, and evaluate its performance on a standard hold-out split.

Frequently Asked Questions about aeon

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I classify multivariate time series data using machine learning?▼

Time series classification models like ROCKET and InceptionTime assign labels to multivariate sequences by learning temporal patterns. You can train these estimators and evaluate performance using specialized metrics on a hold-out split.

Can I detect anomalies and segment change points in sensor streams?▼

Anomaly detection and segmentation identify unusual points, subsequences, or regime changes in sensor streams. These algorithms process temporal data to isolate operational shifts or scientific signal deviations without manual thresholding.

What is the best way to forecast future values from univariate temporal data?▼

Forecasting future values from univariate temporal data uses statistical forecasters and deep learning models. These methods predict continuous targets by analyzing historical sequences and applying feature-based regressors.

Does this approach work with scikit-learn workflows for temporal classification?▼

Yes, time series algorithms provide scikit-learn compatible APIs for temporal classification and regression. This compatibility allows you to integrate sequence estimators directly into existing machine learning pipelines and workflows.

How do I find motifs and compute elastic distances for temporal data?▼

Similarity search finds motifs and computes alignment-aware elastic distances within temporal data. These transformations convert raw univariate or multivariate sequences into informative features for downstream machine learning tasks.

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