Build and deploy time-series machine learning models with Python.
npx skills add https://github.com/crazymsn/academic-skills --skill aeon-crazymsn
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/aeon Command: npx skills add https://github.com/crazymsn/academic-skills --skill aeon-crazymsn
Aeon provides a unified, scalable toolkit to perform time-series machine learning tasks across classification, regression, clustering, forecasting, anomaly detection, and related workflows, simplifying development and experimentation.
Run a basic Aeon time-series workflow on your dataset to obtain a quick result.
High-intent search queries and answers about installing and using this skill.
Time-series classification and anomaly detection on multivariate data are handled through modular feature extraction and model training pipelines that support rapid prototyping and evaluation.
Building a time-series forecasting pipeline compatible with scikit-learn involves using scikit-learn-style interfaces for transformations and feature extraction to enable rapid prototyping and deployment.
Yes, you can use this time-series toolkit for both univariate and multivariate clustering tasks, as it supports end-to-end workflows for training and evaluating models across various time-series data structures.
The aeon toolkit supports motif discovery and pattern finding in temporal data, enabling users to identify recurring sequences and anomalies within their univariate or multivariate datasets.
Limitations for time-series regression and motif discovery workflows depend on dataset scale, as the Python-based modular pipelines require sufficient computational resources to process multivariate temporal data efficiently.
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