What is the Aeon skill used for?

It is used for specialized machine learning tasks involving time-indexed data, such as forecasting, classification, clustering, and pattern recognition in sequences.

Is this skill suitable for motif discovery?

Yes, it includes similarity search capabilities like STOMP for finding repeated patterns (motifs) or unusual subsequences (discords) in time series.

Can I use deep learning for time series with this skill?

Absolutely. The skill includes support for deep learning architectures like InceptionTime, FCN, and ResNet specifically optimized for temporal data.

How do I handle missing values in my time series data?

The skill provides best practices for data preparation, including using Aeon's internal imputation tools to handle missing values before analysis.

Does this skill support scikit-learn?

Yes, Aeon is designed to be fully scikit-learn compatible, allowing you to use its transformers and estimators within standard Python ML pipelines.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: ovachiever

byovachiever

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Data Science & ML

Performs advanced time series machine learning tasks including classification, forecasting, and anomaly detection using the specialized Aeon toolkit.

About

Aeon is a comprehensive Python toolkit designed specifically for temporal data analysis, offering state-of-the-art algorithms that go beyond standard machine learning approaches. This skill enables developers to seamlessly implement time series classification, regression, clustering, and forecasting within their workflows, providing specialized distance metrics and deep learning architectures tailored for sequential patterns. Whether identifying motifs in financial data or detecting anomalies in sensor logs, this skill provides the domain-specific patterns and best practices needed for robust temporal modeling with scikit-learn compatible APIs.

Key Features

Advanced time series classification and regression using ROCKET and InceptionTime
15 GitHub stars
Comprehensive forecasting methods including ARIMA and TCN networks
Specialized temporal distance metrics like Dynamic Time Warping (DTW)
Automated anomaly detection and temporal segmentation algorithms
Extensive feature extraction via Catch22 and Rocket transformers

Use Cases

Identifying rare anomalies or change points in IoT sensor streams
Predicting future values in financial or industrial time series data
Classifying temporal patterns for medical signal analysis or activity recognition

About

Key Features

Advanced time series classification and regression using ROCKET and InceptionTime
15 GitHub stars
Comprehensive forecasting methods including ARIMA and TCN networks
Specialized temporal distance metrics like Dynamic Time Warping (DTW)
Automated anomaly detection and temporal segmentation algorithms
Extensive feature extraction via Catch22 and Rocket transformers

Use Cases

Identifying rare anomalies or change points in IoT sensor streams
Predicting future values in financial or industrial time series data
Classifying temporal patterns for medical signal analysis or activity recognition