On the Evaluation of Hierarchical Forecasts

The aim of this note is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the statistical structure of the hierarchy and the application context. We discuss four relevant dimensions for researchersand analysts: the scale and units of time series, the issue of sparsity, the decision context and the importance of multiple evaluation windows. We conclude with a series of practical recommendations.