Add a Nixtla global gradient-boosting engine to the mlforecast app#24
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dnplkndll wants to merge 1 commit into
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Add a Nixtla global gradient-boosting engine to the mlforecast app#24dnplkndll wants to merge 1 commit into
dnplkndll wants to merge 1 commit into
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The mlforecast app gains a second, selectable engine via the new parameter forecast.MachineLearning_engine (default "orbit", unchanged behaviour): - "orbit": the existing per-series Bayesian model. - "nixtla": a single global LightGBM model (mlforecast + lightgbm, needs the libgomp1 system library) trained across all forecast records, using lag, rolling-window and calendar features. Series with too little history, or a failed fit, fall back to the statistical forecast exactly like orbit. The forecasting core lives in freppledb/mlforecast/nixtla.py with no dependency on the planning engine, so it is covered by unit tests. The statistical forecast solver skips the new parameter, and the app documentation describes both engines and their dependencies.
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Fork-internal PR to run CI before upstreaming to frePPLe/frepple.
Adds an opt-in
nixtlaengine (global LightGBM via the Nixtla mlforecast library) to the mlforecast app, selectable withforecast.MachineLearning_engine(defaultorbit, unchanged). Validated end-to-end on thedemo_ai_forecastingfixture; core unit-tested. Full upstream description inmlforecast-nixtla-PR.md.