skills.rest
RamanEbrahimi
RamanEbrahimiCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

aeon

Automate time series analysis with Python algorithms for classification, forecasting, and anomaly detection.

3|Updated Apr 17, 2026
Search Tags:#machine learning#time series#regression#forecasting#classification#anomaly detection#clustering
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill aeon-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aeon
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/aeon
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill aeon-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, numpy, pandas, matplotlib, seaborn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Aeon tackles the complexities of time series analysis by providing a suite of algorithms tailored for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Core Features & Use Cases

  • Time Series Classification: Categorize time series into classes, suitable for diverse temporal data.
  • Time Series Regression: Predict continuous values, ideal for forecasting and trend analysis.
  • Time Series Clustering: Group similar time series without labels, useful for pattern discovery.
  • Forecasting: Predict future values, essential for business planning and resource allocation.
  • Anomaly Detection: Identify unusual patterns or outliers in time series data.
  • Segmentation: Partition time series into regions with change points, useful for regime change detection.
  • Similarity Search: Find similar patterns within or across time series, ideal for data exploration and comparison.
  • Use Case: Imagine you have a time series of stock prices. Use Aeon to forecast future price movements and identify potential market anomalies.

Quick Start

To start using Aeon, simply run the command 'aeon classify --dataset gunpoint --split train'. This will train a classification model on 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 anomaly detection in Python?▼

Time series forecasting and anomaly detection in Python can be performed using algorithms for classification, regression, and clustering. Aeon provides specialized time series analysis algorithms that detect outliers and predict future values using scikit-learn and pandas.

Can I use pandas and scikit-learn for time series classification?▼

Yes, you can use pandas and scikit-learn for time series classification. Aeon integrates with these Python libraries to categorize temporal data into classes, allowing you to train models on datasets like GunPoint using standard Python workflows.

What is the best way to segment time series data for regime change detection?▼

The best way to segment time series data for regime change detection is using partitioning algorithms. Aeon offers time series segmentation algorithms that partition data into regions with change points, ideal for identifying shifts in temporal patterns.

How do I group similar time series without labels for pattern discovery?▼

To group similar time series without labels for pattern discovery, use unsupervised clustering algorithms. Aeon provides time series clustering algorithms that group similar temporal data together, useful for finding hidden patterns across datasets.

Do I need numpy and matplotlib to analyze time series similarity?▼

You need numpy and matplotlib to process and visualize time series similarity search results. Aeon requires scikit-learn, numpy, pandas, matplotlib, and seaborn as dependencies to find similar patterns within or across time series data.

Related Skills

Explore More Data & Analytics Skills→
Axel-DaMageAxel-DaMage
2

pandas-pro

Automate pandas DataFrame operations for cleaning, aggregation, and time series analysis.

Community
Advanced
robotlearning123robotlearning123

seaborn

Generate statistical visualizations like scatter plots and histograms with Seaborn.

Community
Advanced
LeonChaoXLeonChaoX
783

aeon

Build scikit-learn compatible time-series models for classification, regression, clustering, forecasting, and anomaly detection.

Community
Intermediate
galeepgaleep

aeon

Run machine learning algorithms for time series analysis with the aeon package.

Community
Advanced