A0759
Title: Scalable dynamic hierarchical forecast reconciliation
Authors: Ross Hollyman - University of Bath (United Kingdom) [presenting]
Abstract: A dynamic approach is introduced to probabilistic hierarchical forecasting at scale. The model differs from the existing literature in this area in several important ways. Firstly, the weights allocated to the base forecasts in forming the combined, reconciled forecasts are explicitly allowed to vary over time. Secondly, the assumption is dropped, nearly ubiquitous in the literature, that in-sample base forecasts are appropriate for determining these weights and use out-of-sample forecasts instead. Most existing probabilistic reconciliation approaches rely on time-consuming sampling-based techniques and, therefore, do not scale well (or at all) to large data sets. This problem is addressed in two main ways: firstly, by developing a closed estimator of covariance structure appropriate to hierarchical forecasting problems, and secondly, by decomposing large hierarchies into components that can be reconciled separately.
