Solve time-series machine learning tasks with a scikit-learn-compatible Python API.
npx skills add https://github.com/jackspace/ClaudeSkillz --skill aeon
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-aeon Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill aeon
This Skill provides time series ML patterns using Aeon: classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.
Install aeon and run a small time-series classifier.
High-intent search queries and answers about installing and using this skill.
Time series classification uses machine learning algorithms to assign temporal sequences to predefined categories. Aeon provides scikit-learn compatible classifiers including Rocket and distance-based methods, enabling rapid prototyping on univariate and multivariate sequential data with built-in feature extraction and algorithm catalogs.
Yes, Aeon supports forecasting through algorithms like ARIMA and other transformations designed for temporal prediction. It handles time-indexed observations and provides end-to-end workflows from feature extraction through model deployment for univariate and multivariate series.
Anomaly detection in time series identifies unusual patterns or outliers in sequential observations. Aeon provides anomaly detection capabilities alongside its classification, clustering, and similarity search tools, integrated within a scikit-learn compatible Python API for consistent workflows.
Aeon includes clustering and segmentation algorithms for grouping temporal patterns and partitioning sequences. These tools work with both univariate and multivariate data, complementing classification, regression, and similarity search for comprehensive time series analysis.
Similarity search locates matching patterns or motifs within temporal data using distance-based analytics. Aeon provides distance measures like DTW and catalog algorithms for discord and motif detection, enabling pattern discovery across univariate and multivariate sequences.
Aeon includes dataset loading utilities and benchmarking capabilities for rapid prototyping and algorithm evaluation. Users can access standard datasets and compare multiple algorithms from the catalog across classification, regression, clustering, and forecasting tasks.
dralkhTrain scikit-learn compatible time series classifiers, forecasters, and clusterers with aeon 1.x APIs.
robotlearning123Analyze time series with machine learning methods like forecasting and anomaly detection.
dkyazzentwatwaCompare machine learning models using cross-validation and performance metrics.
vasilyu1983Forecast time series data with LightGBM, Transformers, and SHAP.
