Classify, forecast, and detect anomalies in time series data.
npx skills add https://github.com/jimmc414/Kosmos --skill aeon-jimmc414
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/aeon Command: npx skills add https://github.com/jimmc414/Kosmos --skill aeon-jimmc414
This Skill empowers users to perform complex time series analysis, including classification, regression, forecasting, and anomaly detection, by leveraging a comprehensive suite of state-of-the-art algorithms.
Use the aeon skill to classify the time series data found in the file 'sensor_readings.csv'.
High-intent search queries and answers about installing and using this skill.
Time series classification categorizes temporal data into predefined classes using scikit-learn compatible APIs. This toolkit supports both univariate and multivariate time series analysis, leveraging specialized algorithms and distance metrics specifically designed for sequential sensor data.
Anomaly detection in sequential data identifies unusual patterns or outliers using specialized temporal algorithms. You can apply these techniques to detect faults in industrial machinery vibrations or find unexpected spikes in continuous sensor readings.
Yes, time series forecasting is supported through scikit-learn compatible APIs. You can predict future values based on historical patterns using specialized algorithms and distance metrics built specifically for handling temporal data dependencies.
Time series regression is fully supported for both univariate and multivariate datasets. The toolkit provides specialized algorithms and distance metrics optimized for temporal data, allowing you to model relationships between sequential variables and continuous outcomes.
Beyond forecasting and classification, the toolkit supports clustering, anomaly detection, segmentation, and similarity search. These tasks leverage specialized distance metrics for temporal data, enabling comprehensive analysis of sequential patterns across various machine learning applications.
No external dependencies are required to perform time series similarity search and segmentation. The toolkit operates independently with scikit-learn compatible APIs, using specialized algorithms and distance metrics without requiring additional library installations.
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