Is Aeon compatible with scikit-learn?

Yes, Aeon follows the scikit-learn API design, allowing it to integrate seamlessly with standard ML pipelines, transformers, and estimators.

Can this skill handle both univariate and multivariate data?

Yes, Aeon is designed to handle both univariate and multivariate time series data for analysis, feature extraction, and modeling.

What is the Aeon Claude Code skill used for?

It is used for specialized time series machine learning tasks like classification, forecasting, and anomaly detection that require algorithms specifically designed for temporal or sequential data.

Does the Aeon skill support deep learning?

Yes, it includes various neural architectures specifically optimized for time series, such as InceptionTime, ResNet, and FCN classifiers.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: Activer007

byActiver007

データサイエンスとML

Provides a comprehensive suite of specialized algorithms for time series machine learning including classification, forecasting, and anomaly detection.

概要

Aeon is a scikit-learn compatible Python toolkit specifically designed for handling temporal data and sequential patterns that require more than standard machine learning approaches. It offers state-of-the-art implementations for time series classification, regression, clustering, and similarity search, utilizing both traditional statistical methods and modern deep learning architectures. This skill empowers developers to perform complex temporal analysis, extract meaningful features from time-indexed observations, and implement robust forecasting models within a familiar API ecosystem.

主な機能

Advanced forecasting tools including ARIMA and TCN networks
State-of-the-art time series classification and regression algorithms
Specialized temporal distance metrics like DTW, DDTW, and Shape DTW
Full compatibility with scikit-learn pipelines and API standards
0 GitHub stars
Robust anomaly detection and segmentation for temporal sequences

ユースケース

Predicting future values in financial or sensor-based time series datasets
Classifying sequential data such as medical signals or physical activity logs
Identifying unusual patterns or outliers in industrial monitoring systems

概要

主な機能

Advanced forecasting tools including ARIMA and TCN networks
State-of-the-art time series classification and regression algorithms
Specialized temporal distance metrics like DTW, DDTW, and Shape DTW
Full compatibility with scikit-learn pipelines and API standards
0 GitHub stars
Robust anomaly detection and segmentation for temporal sequences

ユースケース

Predicting future values in financial or sensor-based time series datasets
Classifying sequential data such as medical signals or physical activity logs
Identifying unusual patterns or outliers in industrial monitoring systems