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name: model-selection description: "Choose and configure StatsForecast model classes and direct model APIs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Model selection

Use this sub-skill to pick a StatsForecast model class, confirm constructor knobs, and call the direct array API on a model object.

Use this when

• choosing between automatic, manual statistical, baseline, intermittent, volatility, machine-learning, or fallback model classes
• checking constructor highlights, aliases, exogenous support, interval support, or simulation support
• using direct fit, predict, forecast, forward, predict_in_sample, or simulate methods on a model object

Route elsewhere

• panel orchestration, StatsForecast, X_df, cross-validation, persistence, or distributed execution -> core-forecasting
• MSTL feature decomposition or feature generation -> feature-engineering
• local or distributed backend routing -> distributed-execution

Bundled references

• Model catalog
• API reference
• Troubleshooting

Bundled script

• model_catalog.py