Analyze time series data with aeon for classification, forecasting, and anomaly detection.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill aeon-jasrajtulsi
Please help me install this Agent Skill. Skill: aeon Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/aeon Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill aeon-jasrajtulsi
This Skill helps you work with time-indexed data that needs specialized machine learning methods instead of generic tabular approaches, including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.
Use the aeon skill to analyze the attached time series dataset and recommend the best model, preprocessing steps, and evaluation approach for the task.
High-intent search queries and answers about installing and using this skill.
Time series machine learning applies specialized algorithms to classify, cluster, and forecast univariate and multivariate sequence data. This Skill builds scikit-learn compatible workflows rather than relying on generic tabular approaches.
Yes, scikit-learn compatible estimators support multivariate time series anomaly detection. This Skill applies specialized algorithms to detect anomalies, find motifs, and segment changing regimes within temporal datasets.
Benchmarking deep learning models for time series analysis requires evaluating convolutional and recurrent networks against standard datasets and metrics. This Skill supports benchmark evaluation across sequence-oriented research and production experiments.
Yes, Python 3.10 or higher is required to run time series forecasting workflows. The Skill depends on the aeon toolkit, scikit-learn compatible estimators, and optional deep learning extras for advanced models.
Finding motifs and comparing time series requires elastic distances to measure similarity between sequences. This Skill provides signal and pattern analysis capabilities to detect anomalies, find motifs, and perform similarity search.
You should avoid generic tabular methods for time series classification when data has temporal dependencies requiring specialized algorithms. Instead, use dedicated time series methods for univariate or multivariate sequences.
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