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aeon

Perform time-series classification, regression, clustering, forecasting, anomaly detection, and more with Aeon in Python 3.10+.

Updated Jun 6, 2026
Search Tags:#machine learning#time series#regression#forecasting#classification#anomaly detection#segmentation
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill aeon-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/aeon
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill aeon-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Aeon solves the problem of analyzing time series data by providing scalable and robust algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Core Features & Use Cases

  • Time Series Classification and Regression: Offers various convolution-based and deep learning models for classifying or predicting values from time series.
  • Clustering: Includes time series clustering algorithms like K-means, which can identify similar patterns in unlabelled time series data.
  • Forecasting: Provides models like ARIMA and TCNForecaster for forecasting future values of time series data.
  • Anomaly Detection: Offers tools for detecting unusual patterns in time series data.
  • Segmentation: Can partition time series into regions with distinct characteristics, identifying change points and boundaries.
  • Similarity Search: Allows finding similar patterns within or across time series.
  • Use Case: If you have sensor data and you want to detect anomalies, you can use Aeon's anomaly detection algorithms.

Quick Start

Train and use the Aeon classifier to predict future values from a time series:

from aeon.regression.convolution_based import RocketRegressor
from aeon.datasets import load_regression

X_train, y_train = load_regression("Covid3Month", split="train")
X_test, y_test = load_regression("Covid3Month", split="test")

reg = RocketRegressor()
reg.fit(X_train, y_train)
predictions = reg.predict(X_test)

Frequently Asked Questions about aeon

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

FAQPage Schema
How do I detect anomalies in time series sensor data?▼

You can detect anomalies in sensor data by applying time series anomaly detection algorithms to identify unusual patterns. Aeon provides robust algorithms specifically designed to locate abnormal deviations within sequential sensor readings.

What is time series segmentation and when do I need it?▼

Time series segmentation partitions sequential data into regions with distinct characteristics, identifying change points and boundaries. You need it when analyzing sequential data to detect shifts in behavior or locate distinct operational states.

Can I perform time series forecasting using Python?▼

You can perform time series forecasting in Python using models like ARIMA and TCNForecaster to predict future values. Aeon provides scalable algorithms that apply to various forecasting tasks in fields like finance and energy.

How do I cluster unlabelled time series data?▼

You can cluster unlabelled time series data using algorithms like time series K-means to identify similar patterns. Aeon includes clustering algorithms that group unlabelled sequential data based on shape similarity.

Does Aeon require a specific Python environment setup?▼

Aeon requires Python 3.10 or higher and the aeon package installed via uv pip install. You need this specific Python environment setup to run the scalable time series machine learning algorithms successfully.

What is the best way to classify time series data?▼

The best way to classify time series data is using convolution-based or deep learning models. Aeon offers various classification models that predict categories from sequential data, handling tasks across fields like environmental science.

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