skills.rest
vadimcomanescu
vadimcomanescuCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

aeon

Train ROCKET-based classifiers on time series datasets with scikit-learn compatible APIs.

24|8|Updated Jan 14, 2026
Search Tags:#forecasting#time-series#classification#anomaly-detection#aeon#scikit-learn#feature-extraction
One-click install
npx skills add https://github.com/vadimcomanescu/codex-skills --skill aeon-vadimcomanescu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/vadimcomanescu/codex-skills/tree/main/skills/.curated/data/aeon
Command: npx skills add https://github.com/vadimcomanescu/codex-skills --skill aeon-vadimcomanescu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a comprehensive toolkit to analyze, model, and extract insights from temporal data so teams can classify, forecast, detect anomalies, cluster, segment, and search similarity patterns without building pipelines from scratch.

Core Features & Use Cases

  • Classification & Regression: Scikit-learn compatible estimators for time series classification and regression, suitable for sensor data, finance, and medical signals.
  • Forecasting & Benchmarking: Classical and deep forecasters with forecasting horizon handling, evaluation metrics, and benchmarking utilities for reproducible experiments.
  • Anomaly Detection & Similarity Search: Matrix profile methods, STOMP, and specialized detectors for point and subsequence anomalies plus motif discovery and nearest-neighbor subsequence search.
  • Feature Extraction & Transforms: ROCKET/MiniRocket feature transformers, Catch22 statistics, distance metrics, and preprocessing pipelines for robust feature engineering.
  • Use Case Example: Train a RocketClassifier on multivariate wearable sensor data to detect activities, use ROCKET features for downstream tree-based models, and evaluate with standard benchmarking splits.

Quick Start

Use the aeon skill to train a ROCKET-based classifier on your time series dataset and evaluate its accuracy on the provided test split.

Frequently Asked Questions about aeon

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

FAQPage Schema
How do I train a time series classifier on multivariate sensor data using scikit-learn compatible APIs?▼

Train a time series classifier using scikit-learn compatible estimators like the ROCKET-based classifier to detect activities and evaluate accuracy on provided test splits.

What methods are available for anomaly detection and similarity search in temporal data streams?▼

Anomaly detection and similarity search utilize matrix profile methods, STOMP, and specialized detectors to identify point and subsequence anomalies, plus motif discovery and nearest-neighbor subsequence search.

How do I extract features from time series data for downstream machine learning models?▼

Extract time series features using ROCKET and MiniRocket feature transformers, Catch22 statistics, specialized distance metrics, and preprocessing pipelines to build robust feature engineering workflows.

Can I benchmark classical and deep forecasters for business metrics with this toolkit?▼

Benchmark classical and deep forecasters using forecasting horizon handling, evaluation metrics, and reproducible benchmarking utilities to assess business metric forecasting performance.

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

The toolkit supports clustering, segmentation, and similarity search workflows alongside classification, regression, forecasting, and anomaly detection for both univariate and multivariate temporal data.

Related Skills

Explore More Data & Analytics Skills→
CRAG666CRAG666
46

aeon

Train scikit-learn compatible time series models for classification, forecasting, and anomaly detection.

Community
Intermediate
weirdgmeweirdgme
4

platform-engineering

Design and implement scalable platform engineering patterns for internal developer platforms.

Community
Advanced
yf8578yf8578
1

aeon

Perform time series classification, forecasting, and anomaly detection with scikit-learn compatible APIs.

Community
Advanced
Yezez9Yezez9

aeon

Classify time series data with scikit-learn compatible algorithms.

Community
Advanced