[timeseries] Implement recurrent forecasting model based on TabularPredictor and MLForecast #3177
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shchur
changed the title
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Job PR-3177-eeac00a is done. |
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Job PR-3177-a1ee41c is done. |
| @@ -38,6 +39,7 @@ | |||
| AutoETS=AutoETSModel, | |||
| AutoARIMA=AutoARIMAModel, | |||
| DynamicOptimizedTheta=DynamicOptimizedThetaModel, | |||
| RecursiveTabular=RecursiveTabularModel, | |||
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which presets are we intended to add for this model?
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I plan to update these in a follow-up PR.
| ) -> TimeSeriesDataFrame: | ||
| predictions = self._predict_without_quantiles(data, known_covariates) | ||
| for q in self.quantile_levels: | ||
| predictions[str(q)] = predictions["mean"] + self.quantile_adjustments[q] |
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Wondering how the quantile adjustment is done
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Here we just use the empirical distribution of the quantiles to generate dummy quantiles. I will add another tree-based model that is trained to predict the quantiles using the wQL loss in a future PR.
Description of changes:
RecursiveTabularModelthat uses theTabularPredictorto forecast time series 1 step into the future. The overall forecast is obtained by unrolling the model predictions over time.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.