Perform time series classification, regression, forecasting, and anomaly detection with scikit-learn compatible APIs.
npx skills add https://github.com/Sologa/codex-pipeline --skill aeon-sologa
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/aeon Command: npx skills add https://github.com/Sologa/codex-pipeline --skill aeon-sologa
This Skill empowers users to perform complex time series analysis, including classification, regression, forecasting, anomaly detection, and clustering, using state-of-the-art algorithms.
Use the aeon skill to classify the time series data in the file 'sensor_readings.csv' using the RocketClassifier.
High-intent search queries and answers about installing and using this skill.
Time series forecasting predicts future values by analyzing historical patterns and trends. This toolkit provides specialized algorithms to model temporal dependencies and generate future value predictions from sequential data.
Anomaly detection in multivariate sensor data identifies unusual patterns or outliers across multiple variables. This toolkit supports multivariate time series analysis to pinpoint deviations and anomalies in sequential sensor readings.
Classifying temporal data like clickstream sequences involves categorizing sequential user actions. This toolkit offers state-of-the-art algorithms, including the RocketClassifier, to predict categories from temporal patterns.
Yes, this time series toolkit provides scikit-learn compatible APIs. You can integrate these specialized algorithms for classification, regression, and clustering directly into existing scikit-learn workflows and pipelines.
Clustering groups similar time series data together by identifying shared temporal patterns. This toolkit includes specialized algorithms to segment and cluster univariate and multivariate sequential datasets.
Use specialized time series algorithms when analyzing univariate or multivariate sequential data with temporal dependencies. Standard regression often ignores time-based patterns, whereas these algorithms extract features and model sequential relationships directly.
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