A top-down approach to hierarchically coherent probabilistic forecasting

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to obtain coherent predictions for a large number of correlated time series that are arranged in a pre-specified tree hierarchy. In this paper, we present a novel probabilistic top-down approach to hierarchical forecasting that uses a novel attention-based RNN model to learn the distribution of the proportions according to which each parent prediction is split among its children nodes at any point in time. It also relies on an independent probabilistic forecasting model for the (univariate) root time series that can be generated using a sequence-to-sequence model, or even from traditional autoregressive-style univariate forecasting models , and The resulting forecasts are computed in a top-down fashion and are naturally coherent, and also support probabilistic predictions over all time series in the hierarchy. We provide theoretical justification for the superiority of our top-down approach compared to traditional bottom-up hierarchical modeling. Finally, we experiment on several public datasets and demonstrate significantly improved probabilistic forecasts, compared to state-of-the-art probabilistic hierarchical models.

• Machine Intelligence