Perform time series classification, forecasting, and anomaly detection with scikit-learn compatible APIs.
npx skills add https://github.com/robinbarvaag/poynt --skill aeon-robinbarvaag
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/aeon Command: npx skills add https://github.com/robinbarvaag/poynt --skill aeon-robinbarvaag
This Skill empowers users to perform complex time series machine learning tasks, from forecasting and anomaly detection to classification and clustering, by leveraging a comprehensive suite of state-of-the-art algorithms.
Use the aeon skill to train a RocketClassifier on the GunPoint dataset and evaluate its accuracy.
High-intent search queries and answers about installing and using this skill.
Time series forecasting with machine learning uses algorithms like ARIMA to predict future values from historical temporal data. This Skill supports univariate and multivariate forecasting through scikit-learn compatible APIs for energy consumption or sensor data prediction.
Anomaly detection in sensor time series data identifies outliers using specialized algorithms. This Skill employs techniques like STOMP for similarity search and anomaly detection, enabling robust identification of unusual patterns in univariate or multivariate temporal streams.
ROCKET and InceptionTime are advanced algorithms used for time series classification tasks. This Skill provides access to these state-of-the-art methods alongside scikit-learn compatible APIs to classify equipment failures or behavioral patterns from historical temporal data.
Multivariate time series analysis is fully supported, allowing simultaneous evaluation of multiple variables. The Skill handles both univariate and multivariate data for classification, regression, forecasting, clustering, and anomaly detection using advanced feature extraction.
Time series clustering and segmentation group temporal data by identifying similar patterns across sequences. This Skill provides algorithms for clustering and segmentation, enabling pattern discovery and structural analysis in temporal datasets.
Feature engineering for time series machine learning utilizes advanced transformations like ROCKET and Catch22 for robust feature extraction. These methods convert raw temporal sequences into structured representations compatible with standard classification and regression algorithms.
CRAG666Train scikit-learn compatible time series models for classification, forecasting, and anomaly detection.
LeonChaoXBuild scikit-learn compatible time-series models for classification, regression, clustering, forecasting, and anomaly detection.
vadimcomanescuTrain ROCKET-based classifiers on time series datasets with scikit-learn compatible APIs.
Lord1EgyptClassify, forecast, and cluster time series with scikit-learn compatible APIs.
