e2eTD forecasts a small set of aggregate series and disaggregates them via copula-based historical proportions, producing coherent probabilistic forecasts for huge retail hierarchies in minutes.
2012.06846 , archivePrefix=
2 Pith papers cite this work. Polarity classification is still indexing.
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Mean scaled score is recommended over rank-based aggregation for model selection on time series datasets because skewness causes non-mean criteria to select misspecified models with short tests.
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End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down
e2eTD forecasts a small set of aggregate series and disaggregates them via copula-based historical proportions, producing coherent probabilistic forecasts for huge retail hierarchies in minutes.
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Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score
Mean scaled score is recommended over rank-based aggregation for model selection on time series datasets because skewness causes non-mean criteria to select misspecified models with short tests.