Why use Aeon instead of standard machine learning libraries?

Aeon uses specialized temporal distance metrics and algorithms designed to capture the specific sequential dependencies found in time series data.

Does Aeon support deep learning?

Absolutely, the skill includes specialized neural network architectures like InceptionTime and ResNet that are specifically optimized for temporal sequences.

What is the Aeon Claude Code skill used for?

It is used for specialized machine learning tasks involving time-indexed data, such as classification, forecasting, clustering, and anomaly detection.

Can I perform multivariate analysis with this skill?

Yes, the Aeon skill supports both univariate and multivariate time series data across its classification, regression, and forecasting modules.

Is this skill compatible with scikit-learn?

Yes, Aeon is built with a scikit-learn compatible API, allowing it to integrate seamlessly into existing Python data science pipelines.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: Sologa

bySologa

データサイエンスとML

Empowers Claude to perform advanced time series machine learning, including classification, forecasting, and anomaly detection using the specialized aeon toolkit.

概要

Aeon is a comprehensive scikit-learn compatible toolkit designed specifically for temporal data and sequential patterns. This skill enables Claude to handle complex time-series tasks—such as multivariate classification, long-term forecasting, and pattern-based clustering—with state-of-the-art algorithms like ROCKET and HIVE-COTE. It is ideal for researchers and developers working with time-indexed observations who require higher accuracy and more specialized distance metrics than standard machine learning libraries provide.

主な機能

Specialized distance metrics including Dynamic Time Warping (DTW)
Advanced forecasting methods for univariate and multivariate data
0 GitHub stars
State-of-the-art time series classification and regression algorithms
Temporal anomaly detection and change-point segmentation
Neural architectures optimized for deep learning on sequences

ユースケース

Classifying complex temporal signals such as ECG, audio, or motion data
Identifying mechanical failures or network intrusions via anomaly detection
Predicting future values in financial, weather, or sensor-based datasets

概要

主な機能

Specialized distance metrics including Dynamic Time Warping (DTW)
Advanced forecasting methods for univariate and multivariate data
0 GitHub stars
State-of-the-art time series classification and regression algorithms
Temporal anomaly detection and change-point segmentation
Neural architectures optimized for deep learning on sequences

ユースケース

Classifying complex temporal signals such as ECG, audio, or motion data
Identifying mechanical failures or network intrusions via anomaly detection
Predicting future values in financial, weather, or sensor-based datasets