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name: core-forecasting description: "Operate normal StatsForecast pandas/polars panel forecasting workflows, including schema, forecasts, fitted values, cross-validation, intervals, exogenous future data, custom columns, persistence, plotting, fallback models, and local n_jobs choices." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

core-forecasting

Use this sub-skill when the user needs the StatsForecast orchestrator for local pandas or polars panel forecasting: build the input schema, construct StatsForecast, run forecasts, get fitted values, perform temporal cross-validation, add intervals, pass future exogenous data, use custom column names, save/load fitted objects, create basic plots, and choose local n_jobs/fallback_model behavior.

Route boundaries

Stay in this sub-skill for:

• Native pandas or polars panels with id, time, target, and optional exogenous columns.
• forecast, fit/predict, fit_predict, forecast_fitted_values, cross_validation, and cross_validation_fitted_values workflows.
• Prediction interval plumbing, including level and ConformalIntervals usage at the orchestrator level.
• Custom id_col, time_col, and target_col names.
• Basic plot, save, and load calls.
• Local process parallelism through n_jobs and primary-model recovery through fallback_model.

Route out when the user is really asking for:

• Which forecasting model family or model constructor to use: load model-selection.
• MSTL decomposition or future seasonal/trend feature generation: load feature-engineering.
• Dask, Ray, Spark, Fugue engines, distributed dataframes, or cluster behavior: load distributed-execution.

Operating sequence

1. Validate the panel layout and time frequency using references/data-formats.md.
2. Pick or confirm model objects elsewhere if model choice is non-trivial, then construct StatsForecast using references/api-reference.md.
3. Select the workflow (forecast vs fit/predict vs fit_predict vs cross-validation) from references/workflows.md.
4. If the panel contains extra columns and any model uses exogenous regressors, build a future X_df with all ids, all future timestamps, and the same exogenous columns.
5. If adding intervals, pass a list-like level and check conformal sample-size requirements before running expensive jobs.
6. If a call fails, diagnose with references/troubleshooting.md before changing model families.
7. To sanity-check an installed runtime without a repository checkout, run scripts/core_forecast_smoke.py; use --help to see optional custom-column, exogenous, and interval checks.

Output expectations

Core StatsForecast calls return the same dataframe family as the native input when possible. Forecast outputs contain id/time columns plus one column per model and, when requested, interval columns named like <model>-lo-80 and <model>-hi-80. Cross-validation outputs add cutoff and observed target values. Fitted-value helpers only work after the corresponding call used fitted=True.