How does this skill help with temporal data?

It provides domain-specific patterns for algorithm selection, such as using ROCKET for speed or InceptionTime for maximum accuracy in time series tasks.

Is Aeon compatible with my existing scikit-learn code?

Absolutely. Aeon is built with scikit-learn compatibility in mind, allowing you to use its transformers and estimators within standard ML pipelines.

Does this skill support deep learning for time series?

Yes, it provides implementation patterns for neural architectures like FCN, ResNet, and InceptionTime specifically tuned for temporal data.

What is the Aeon toolkit?

Aeon is a scikit-learn compatible Python library specifically designed for time series machine learning, including forecasting and classification.

Can I use Aeon for anomaly detection?

Yes, Aeon includes specialized detectors like STOMP to identify unusual patterns or outliers within temporal sequences.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: pur3v4d3r

bypur3v4d3r

•

データサイエンスとML

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

概要

Aeon is a specialized Claude Code Skill designed for advanced temporal data analysis, providing access to state-of-the-art algorithms for complex time series tasks. It bridges the gap between raw sequential data and actionable insights by offering tools for classification, regression, clustering, and forecasting. Whether you are detecting anomalies in sensor data, predicting trends, or segmenting temporal patterns, this skill guides you through optimal algorithm selection and implementation using the aeon Python toolkit, ensuring seamless integration into existing machine learning pipelines through its scikit-learn compatible interface.

主な機能

1 GitHub stars
Temporal Clustering and Similarity Search
High-performance Feature Extraction (ROCKET, Catch22)
Advanced Time Series Classification and Regression
Specialized Temporal Distance Metrics like DTW
Automated Anomaly Detection and Segmentation

ユースケース

Predicting future values in financial, IoT, or sensor datasets
Detecting irregular patterns and outliers in sequential system logs
Categorizing temporal sequences such as movement or medical data

概要

主な機能

1 GitHub stars
Temporal Clustering and Similarity Search
High-performance Feature Extraction (ROCKET, Catch22)
Advanced Time Series Classification and Regression
Specialized Temporal Distance Metrics like DTW
Automated Anomaly Detection and Segmentation

ユースケース

Predicting future values in financial, IoT, or sensor datasets
Detecting irregular patterns and outliers in sequential system logs
Categorizing temporal sequences such as movement or medical data