Reconciling Hierarchical Forecasts via Bayes' Rule
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When time series are organized into hierarchies, the forecasts have to satisfy some summing constraints. Forecasts which are independently generated for each time series (base forecasts) do not satisfy the constraints. Reconciliation algorithms adjust the base forecast in order to satisfy the summing constraints: in general they also improve the accuracy. We present a novel reconciliation algorithm based on Bayes' rule; we discuss under which assumptions it is optimal and we show in extensive experiments that it compares favorably to the state-of-the-art reconciliation methods.
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