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name: forecasting description: "Fit, predict, and diagnose tslearn VARIMA and AutoVARIMA workflows, including variable-length series and seasonal-length caveats." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

Forecasting

Use this sub-skill when the task involves tslearn.forecasting or tslearn.forecasting._arima: fitting VARIMA, fitting AutoVARIMA, forecasting from fitted data, forecasting a fresh variable-length dataset, or diagnosing ARIMA-order, seasonality, and minimum-length failures.

Route the task

• Start from the root tslearn router at ../../SKILL.md for package-wide task routing.
• For constructors, method signatures, shapes, fitted attributes, and minimum-length rules, read references/api-reference.md.
• For tiny reproducible fit, predict, fit_predict, AutoVARIMA, and seasonal workflows, read references/workflows.md.
• For too-short series, stationarity or seasonal-period setup, and convergence or optimizer issues, read references/troubleshooting.md.
• To sanity-check the smallest bundled forecasting workflow, run python scripts/forecasting_smoke.py.
• If the blocker is dataset shaping, NaN padding, resampling, or conversion to a time-series dataset, route to ../data-preparation/.
• If the blocker is forecast scoring such as MAE/MSE/MASE or backend/performance details, route to ../metrics-backends/.

Core boundaries

• Cover VARIMA, AutoVARIMA, fit, predict, fit_predict, best_estimator_, variable-length forecasting input, and seasonal/differencing caveats.
• Keep matrix profile, serialization, clustering, and supervised classification outside this sub-skill.
• Keep general preprocessing and metric-selection depth in the sibling sub-skills named above.

Fast starting point

1. Use VARIMA(p, d, q, ...) when the ARIMA orders are already chosen; use AutoVARIMA(...) when the task is order selection plus forecasting.
2. For VARIMA, every series must have at least p + q + d + 1 + seasonal_period real timestamps at both fit and predict time.
3. For AutoVARIMA, validate the same shape/feature rules and expect the selected best_estimator_ to impose its own VARIMA minimum-length rule.
4. Run python scripts/forecasting_smoke.py before adapting examples to larger user data.