Build scikit-learn compatible time series ML models for classification, forecasting, and anomaly detection.
npx skills add https://github.com/shushuzn/Rairos --skill aeon-shushuzn
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/shushuzn/Rairos/tree/main/skills/aeon Command: npx skills add https://github.com/shushuzn/Rairos --skill aeon-shushuzn
It helps you model and extract insight from time-indexed data by providing scikit-learn compatible tools for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.
Ask the AI to detect anomalies in the attached time series by fitting STOMP with an appropriate window size and returning the highest-scoring anomalous points.
High-intent search queries and answers about installing and using this skill.
Time series anomaly detection models like STOMP identify outliers and discords by scoring sequential data points against learned motifs. You fit the model with an appropriate window size to return the highest-scoring anomalous points in your temporal data.
Yes, you can build scikit-learn compatible models for time series forecasting and classification. The Skill provides specialized algorithms and feature transformations like ROCKET and Catch22 to convert sequential temporal data into learnable inputs for standard estimators.
Time series similarity search applies specialized distance metrics such as DTW and Euclidean distance to find motifs, discords, and nearest neighbors. This pattern discovery process uses subsequence methods to match similar sequential shapes across your temporal data.
You apply feature extraction and transformations to convert time series data into tabular inputs for downstream estimators. Specialized temporal representations like ROCKET or Catch22 transform univariate and multivariate sequences into learnable features for classification or regression.
Yes, time series clustering and segmentation models group similar series and divide sequential data into change-point regions. These algorithms identify structural shifts and segment temporal data into distinct sequential patterns for further analysis.
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