What is the Aeon skill used for?

The Aeon skill is used for specialized machine learning tasks involving time series data, including classification, regression, clustering, and forecasting.

Can I use this for multivariate time series analysis?

Absolutely. Aeon is designed to handle both univariate and multivariate time series data across various machine learning tasks.

Does this skill support deep learning for time series?

Yes, it provides access to deep learning architectures specifically designed for temporal data, such as InceptionTime, FCN, and ResNet.

Is Aeon compatible with scikit-learn?

Yes, Aeon is built with a scikit-learn compatible API, allowing for seamless integration with standard Python machine learning pipelines.

Aeon Time Series Analysis

Name: Aeon Time Series Analysis
Author: jimmc414

byjimmc414

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324

Data Science & ML

Performs advanced time series machine learning tasks including classification, forecasting, and anomaly detection using scikit-learn compatible APIs.

About

Aeon is a comprehensive Python toolkit for time series machine learning, and this skill enables Claude to expertly apply its state-of-the-art algorithms. It provides a specialized environment for handling temporal data, allowing for complex tasks like sequence classification, future value forecasting, and motif discovery. Whether you are working with univariate sensor data or multivariate financial signals, this skill guides the implementation of high-performance models ranging from fast ROCKET transforms to sophisticated deep learning architectures like InceptionTime.

Key Features

Specialized temporal distance metrics like DTW and ERP
Time series clustering and segmentation capabilities
324 GitHub stars
Deep learning architectures for sequential data analysis
Comprehensive time series classification and regression
Advanced forecasting and anomaly detection algorithms

Use Cases

Classifying complex patterns in medical or scientific research data
Predicting future trends in financial or industrial sensor data
Detecting anomalies or change points in temporal sequences

About

Key Features

Specialized temporal distance metrics like DTW and ERP
Time series clustering and segmentation capabilities
324 GitHub stars
Deep learning architectures for sequential data analysis
Comprehensive time series classification and regression
Advanced forecasting and anomaly detection algorithms

Use Cases

Classifying complex patterns in medical or scientific research data
Predicting future trends in financial or industrial sensor data
Detecting anomalies or change points in temporal sequences