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aeon

Analyze time series data for classification, regression, clustering, forecasting, and anomaly detection.

Updated Mar 15, 2026
Search Tags:#machine learning#time series#regression#forecasting#classification#anomaly detection
One-click install
npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill aeon-sagunkayastha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/machine-learning-ai/aeon
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill aeon-sagunkayastha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexity of analyzing time-ordered data, providing a comprehensive toolkit for extracting insights, making predictions, and understanding patterns within sequential information.

Core Features & Use Cases

  • Time Series Analysis: Perform classification, regression, clustering, forecasting, and anomaly detection on temporal data.
  • Feature Engineering: Extract meaningful features using advanced transformations like ROCKET or statistical methods.
  • Use Case: Predict future stock prices based on historical trading data, classify sensor readings into different activity types, or detect anomalies in network traffic patterns.

Quick Start

Use the aeon skill to classify the time series data from the 'GunPoint' dataset.

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 forecasting and classification in Python?▼

Time series forecasting and classification are executed through a unified Python API that applies state-of-the-art machine learning algorithms to temporal data. This approach directly facilitates predictive tasks on sequential information.

What is the best way to detect anomalies in sequential sensor data?▼

Anomaly detection in sequential sensor data is achieved by applying specialized machine learning algorithms to time series sequences. This allows you to identify unexpected patterns and outliers within temporal information.

How does feature extraction for time series machine learning work?▼

Feature extraction for time series machine learning works by applying advanced transformations like ROCKET or statistical methods to temporal data. This process extracts meaningful features needed to train classification and regression models.

Can I use specialized distance metrics for temporal clustering?▼

Specialized distance metrics for temporal clustering are supported within the time series analysis framework. This allows you to accurately group temporal sequences by measuring the similarity between time-ordered data points.

Do I need any specific dependencies to run time series regression tasks?▼

No specific dependencies are required to run time series regression tasks. The skill provides an advanced machine learning toolkit that operates independently to facilitate regression on temporal data.

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