Organize time-series machine learning workflows with unified APIs for models and evaluation.
npx skills add https://github.com/mkurman/tamux --skill aeon-mkurman
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/aeon Command: npx skills add https://github.com/mkurman/tamux --skill aeon-mkurman
Aeon provides a unified library of time-series machine learning tools, enabling researchers and practitioners to train and evaluate classifiers, regressors, clusterers, and forecasters with consistent interfaces.
Install Aeon and run a quick example to train a classifier on a sample time-series dataset.
High-intent search queries and answers about installing and using this skill.
Time-series classification and forecasting use scikit-learn-like APIs by providing a unified toolkit for training and evaluating models on univariate and multivariate sequences with consistent interfaces.
Time-series anomaly detection and segmentation identify abnormal patterns and structural changes within multivariate sequences using a unified toolkit designed for consistent model evaluation.
Time-series machine learning workflows support both research and production contexts by offering broad algorithm coverage, dataset compatibility, and benchmarking utilities for reproducible experiments.
The best way to compare multiple forecasting models on a time-series dataset is using benchmarking utilities and sklearn-like evaluation interfaces that ensure reproducible experimental results.
This time-series toolkit supports similarity search and clustering for univariate data alongside classification, regression, forecasting, and segmentation within a unified algorithm library.
A unified time-series library provides consistent interfaces for clustering and regression, allowing practitioners to train and evaluate diverse models without learning separate APIs.
crazymsnBuild and deploy time-series machine learning models with Python.
vadimcomanescuTrain ROCKET-based classifiers on time series datasets with scikit-learn compatible APIs.
JosephWoodallAnalyze time-series data for classification, regression, forecasting, and anomaly detection.
RamanEbrahimiAutomate time series analysis with Python algorithms for classification, forecasting, and anomaly detection.
