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Probabilistic, hierarchically coherent forecasting is a key problem in manypractical forecasting applications -- the goal is to obtain coherentprobabilistic predictions for a large number of time series arranged in apre-specified tree hierarchy. In this paper, we present an end-to-end deepprobabilistic model for hierarchical forecasting that is motivated by aclassical top-down strategy. It jointly learns the distribution of the roottime series, and the (dirichlet) proportions according to which each parenttime-series is split among its children at any point in time. The resultingforecasts are naturally coherent, and provide probabilistic predictions overall time series in the hierarchy. We experiment on several public datasets anddemonstrate significant improvements of up to 26% on most datasets compared tostate-of-the-art baselines. Finally, we also provide theoretical justificationfor the superiority of our top-down approach compared to the more traditionalbottom-up modeling.

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting