Does this skill support multivariate time series?

Yes, the Aeon skill provides guidance and implementation patterns for both univariate and multivariate time series analysis.

What are the recommended algorithms for fast prototyping?

For quick results with high performance, the skill recommends using ROCKET variants like MiniRocketClassifier for classification and MiniRocketRegressor for regression.

What makes Aeon different from standard scikit-learn?

While scikit-learn compatible, Aeon utilizes specialized algorithms designed specifically to handle the temporal dependencies and sequential patterns in time series data that standard ML models often ignore.

Can I use deep learning with this skill?

Absolutely. Aeon includes specialized neural architectures such as InceptionTime, ResNet, and FCN specifically optimized for time series tasks.

Aeon Time Series Machine Learning

Name: Aeon Time Series Machine Learning
Author: Microck

byMicrock

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数据科学与机器学习

Provides specialized algorithms and workflows for advanced time series tasks including forecasting, classification, and anomaly detection.

关于

Aeon is a comprehensive scikit-learn compatible Python toolkit specifically engineered for the complexities of time series machine learning. This skill enables Claude to implement state-of-the-art algorithms for temporal data, ranging from forecasting future values and detecting sequence anomalies to clustering patterns and performing similarity searches. It is an essential resource for developers and data scientists working with univariate or multivariate time-indexed observations that require specialized temporal distance metrics and transformation techniques beyond standard machine learning approaches.

主要功能

81 GitHub stars
Time series clustering, segmentation, and motif discovery
Specialized temporal distance metrics like DTW and MSM
Deep learning architectures optimized for sequential data
Advanced forecasting and anomaly detection capabilities
State-of-the-art time series classification and regression models

使用场景

Identifying equipment failures or security breaches via anomaly detection
Categorizing biological or industrial signals using specialized classifiers
Predicting future financial trends or resource demand through forecasting

关于

主要功能

81 GitHub stars
Time series clustering, segmentation, and motif discovery
Specialized temporal distance metrics like DTW and MSM
Deep learning architectures optimized for sequential data
Advanced forecasting and anomaly detection capabilities
State-of-the-art time series classification and regression models

使用场景

Identifying equipment failures or security breaches via anomaly detection
Categorizing biological or industrial signals using specialized classifiers
Predicting future financial trends or resource demand through forecasting