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Yezez9
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aeon

Classify time series data with scikit-learn compatible algorithms.

Updated Mar 10, 2026
Search Tags:#machine learning#time series#data analysis#forecasting#classification#anomaly detection
One-click install
npx skills add https://github.com/Yezez9/Research-Agent --skill aeon-yezez9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/aeon
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill aeon-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill empowers you to tackle complex time series data challenges, from forecasting future trends to identifying anomalies and classifying patterns, all within a unified, scikit-learn compatible framework.

Core Features & Use Cases

  • Time Series Classification: Categorize temporal data using state-of-the-art algorithms.
  • Forecasting: Predict future values with models like ARIMA and TCNs.
  • Anomaly Detection: Identify unusual patterns or outliers in sequential data.
  • Clustering: Group similar time series patterns.
  • Use Case: Analyze sensor data to predict equipment failures, forecast stock market trends, or classify different types of audio signals.

Quick Start

Use the aeon skill to classify the time series data in the file 'sensor_readings.csv' 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 perform time series classification on sensor data?▼

Time series classification categorizes temporal data using algorithms like the RocketClassifier. You can classify sensor readings from a CSV file using scikit-learn compatible APIs provided by this toolkit.

Can I forecast future values using ARIMA and deep learning networks?▼

Yes, forecasting predicts future values using models like ARIMA and Temporal Convolutional Networks (TCNs). These algorithms handle both univariate and multivariate time series data.

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

Anomaly detection identifies unusual patterns or outliers in sequential data. This toolkit provides state-of-the-art algorithms with specialized distance metrics to isolate these deviations.

Does this time series toolkit work with the scikit-learn API?▼

Yes, the toolkit offers scikit-learn compatible APIs for classification, regression, clustering, forecasting, anomaly detection, and segmentation, ensuring seamless integration into existing machine learning workflows.

Can I use this for multivariate time series clustering?▼

Yes, this toolkit supports both univariate and multivariate time series analysis. You can group similar temporal patterns using specialized clustering algorithms and distance metrics.

When do I need specialized distance metrics for time series analysis?▼

Specialized distance metrics are required when comparing complex sequential data patterns for classification or clustering. They improve accuracy over standard metrics by accounting for temporal shifts and distortions.

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