Can I use deep learning with the Aeon skill?

Absolutely. Aeon includes specialized neural architectures like InceptionTime, FCN, and ResNet specifically optimized for temporal data patterns.

Which algorithms are best for fast time series classification?

For fast prototyping and high performance, the skill recommends using ROCKET variants like MiniRocketClassifier or Arsenal.

What is the Aeon skill for Claude?

The Aeon skill provides specialized capabilities for performing time series machine learning, including classification, clustering, and forecasting, within the Claude Code environment.

Is Aeon compatible with scikit-learn?

Yes, Aeon is designed with a scikit-learn compatible API, allowing you to use familiar fit, transform, and predict patterns in your time series workflows.

Does this skill support multivariate time series?

Yes, Aeon is built to handle both univariate and multivariate time series analysis across all its core learning tasks.

Aeon Time Series Machine Learning

Name: Aeon Time Series Machine Learning
Author: Weiwei-Mao

byWeiwei-Mao

데이터 과학 및 ML

Provides specialized machine learning algorithms for time series tasks including forecasting, classification, and anomaly detection using scikit-learn compatible APIs.

소개

Aeon is a comprehensive Python toolkit that extends traditional machine learning capabilities to the temporal domain, offering state-of-the-art algorithms for analyzing time-indexed data. This skill enables Claude to implement complex temporal workflows—such as motif discovery, change point detection, and high-accuracy classification—using a standardized interface that integrates seamlessly with existing data science pipelines. It is particularly valuable for developers needing to analyze sequences, signals, or trends where standard flat-data ML approaches are insufficient.

주요 기능

0 GitHub stars
Built-in deep learning architectures specifically designed for time-indexed observations
Comprehensive suite for forecasting, anomaly detection, and temporal segmentation
Scikit-learn compatible API for seamless integration into existing ML workflows
Specialized temporal distance metrics including Dynamic Time Warping (DTW) and MSM
Advanced time series classification and regression using ROCKET and HIVE-COTE algorithms

사용 사례

Predicting future trends and values in financial, weather, or sensor data streams
Detecting anomalies and unusual patterns in system logs, medical signals, or industrial IoT
Classifying temporal sequences for signal processing and gesture recognition

소개

주요 기능

0 GitHub stars
Built-in deep learning architectures specifically designed for time-indexed observations
Comprehensive suite for forecasting, anomaly detection, and temporal segmentation
Scikit-learn compatible API for seamless integration into existing ML workflows
Specialized temporal distance metrics including Dynamic Time Warping (DTW) and MSM
Advanced time series classification and regression using ROCKET and HIVE-COTE algorithms

사용 사례

Predicting future trends and values in financial, weather, or sensor data streams
Detecting anomalies and unusual patterns in system logs, medical signals, or industrial IoT
Classifying temporal sequences for signal processing and gesture recognition