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ts-agents
ts-agents contains 6 collected skills from fnauman, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
Convert ts-agents workflow outputs, run manifests, plots, JSON, CSV files, and short Markdown reports into stakeholder-facing Markdown, Quarto, HTML, or PDF reports. Use when the user asks for a report, executive summary, research appendix, or reusable analysis deliverable from existing ts-agents artifacts.
End-to-end workflow for labeled-stream activity recognition: prepare or download data, run window-size selection, evaluate a windowed classifier, and produce plots + a short report. Use when you need a reproducible CLI workflow artifact or evaluation bundle.
Forecast/predict future values of a time series, choose reasonable baselines, and compare forecasting methods on arbitrary series loaded from ts-agents data.
Supervised time series classification: choose and run classifiers (KNN/DTW, ROCKET variants, HIVE-COTE), compare models, and report accuracy. Use when the user asks to classify/categorize time series, build a classifier, or compare time series classification algorithms.
Decompose a time series into trend/seasonal/residual components (STL, MSTL, Holt-Winters). Use when the user asks about trend, seasonality, detrending, or wants residuals for anomaly detection/forecasting.
Quick EDA and diagnostics for a time series: descriptive stats, autocorrelation, and periodicity. Use when the user asks "what does this series look like?", "is there seasonality?", "what's the period?", or before choosing decomposition/forecasting parameters.