What types of time series tasks can Aeon handle?

Aeon supports a wide range of tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Can I use deep learning with the Aeon skill?

Yes, Aeon includes various deep learning architectures such as InceptionTime, ResNet, and FCN specifically optimized for time series classification and regression.

Does this skill support multivariate time series?

Yes, Aeon is designed to handle both univariate and multivariate time series analysis using specialized algorithms optimized for multi-dimensional temporal data.

Is Aeon compatible with scikit-learn?

Yes, Aeon is built with a scikit-learn compatible API, allowing it to integrate seamlessly with standard machine learning pipelines and tools like GridSearchCV.

What are the performance advantages of using Aeon?

Aeon provides state-of-the-art algorithms like ROCKET (for extreme speed) and HIVE-COTE (for maximum accuracy) that outperform traditional ML approaches on temporal data.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: Zehong-Wang

byZehong-Wang

0•

Ciencia de Datos y ML

Implements advanced time series machine learning tasks including forecasting, classification, and anomaly detection using a scikit-learn compatible framework.

Aeon is a comprehensive Python toolkit designed for specialized time series machine learning tasks that go beyond standard ML approaches. It provides a unified interface for state-of-the-art algorithms in temporal data analysis, including classification, regression, clustering, and segmentation. By offering scikit-learn compatible APIs and specialized distance metrics like Dynamic Time Warping (DTW), Aeon enables developers to efficiently build, train, and deploy models for complex sequential patterns, whether working with univariate or multivariate datasets.

Características Principales

01High-performance forecasting with ARIMA and deep learning architectures

020 GitHub stars

03Advanced time series classification and regression using ROCKET and InceptionTime

04Sophisticated anomaly detection and change-point segmentation for temporal data

05Specialized temporal distance metrics including DTW, MSM, and elastic measures

06Seamless integration with scikit-learn pipelines and standard Python data science stacks

Casos de Uso

01Detecting equipment failures or security breaches through temporal anomaly detection in sensor data

02Classifying physiological signals like ECG or EEG for medical diagnostics and research

03Predicting future trends in financial markets or demand forecasting for inventory management

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos aeon

For use in Claude.ai and ChatGPT

Download Skill