Forecasting with Deep Temporal Hierarchies

Abstract

In time series analysis and forecasting, the identification of an appropriate model remains a challenging task. Model misspecification can lead to erroneous forecasts and insights. The use of multiple views of the same time series by constructing temporally aggregate levels has been proposed as a way to overcome the model specification and selection uncertainty, with ample empirical evidence of forecast accuracy gains. Temporal Hierarchies is the most popular approach to achieve this, which itself is based on research in hierarchical forecasting. Although there has been substantial progress in this literature, the vast majority of methods rely on a restricted linear combination of different model outputs across the hierarchy. We investigate the use of deep learning to augment temporal hierarchies, relaxing the classical restrictions. Specifically, we look at deep learning for the generation of all the base forecasts, the hierarchical reconciliation, and an end-to-end method that embeds all steps in a single neural network. We inspect the performance of the proposed methods when applied to individual time series, or with global training across complete sets of series. We further investigate the requirements in terms of series set size, illustrating the conditions where deep learning temporal hierarchies outperform conventional temporal hierarchies.