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ml-aeon

Provide a scikit-learn compatible toolkit for time series machine learning.

Updated Mar 13, 2026
Search Tags:#machine learning#time series#forecasting#classification#anomaly detection#clustering#aeon
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-aeon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-aeon
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/ml-aeon
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-aeon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill empowers users to perform advanced machine learning tasks on time series data, from classification and forecasting to anomaly detection and clustering, without needing to implement complex algorithms from scratch.

Core Features & Use Cases

  • Comprehensive Algorithms: Access state-of-the-art models for classification, regression, forecasting, clustering, anomaly detection, and segmentation.
  • Data-Driven Insights: Uncover patterns, predict future trends, and identify anomalies in sequential data.
  • Use Case: Analyze sensor data to predict equipment failure (forecasting), classify customer behavior patterns from clickstream data (classification), or detect fraudulent transactions in financial time series (anomaly detection).

Quick Start

Use the ml-aeon skill to train a RocketClassifier on the GunPoint dataset and evaluate its accuracy.

Frequently Asked Questions about ml-aeon

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

FAQPage Schema
How do I perform machine learning classification on time series data?▼

You can perform time series classification using scikit-learn compatible algorithms like the RocketClassifier. The toolkit provides state-of-the-art models to classify sequential patterns, such as analyzing clickstream data, without implementing algorithms from scratch.

Can I use scikit-learn compatible tools for multivariate time series forecasting?▼

Yes, this toolkit supports both univariate and multivariate time series forecasting. It provides specialized algorithms and distance metrics to predict future trends, such as analyzing sensor data to forecast equipment failure.

What is the best way to detect anomalies in sequential data without coding complex algorithms?▼

Anomaly detection in sequential data is handled through built-in specialized algorithms. You can identify outliers like fraudulent transactions in financial time series directly, bypassing the need to build complex detection logic from scratch.

Does this time series toolkit support clustering and segmentation tasks?▼

Yes, the toolkit supports both clustering and segmentation for time series analysis. You can uncover patterns and group similar sequential data behaviors using its state-of-the-art algorithmic implementations.

Do I need to manually extract features before training a time series model?▼

No, manual feature extraction is not strictly required as the toolkit facilitates feature extraction and transformation internally. It provides specialized distance metrics and algorithms designed to handle univariate and multivariate time series directly.

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