Forecasting with Temporal Hierarchies

George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes, Fotios Petropoulos

Research output: Contribution to journalArticle

48 Citations (Scopus)
126 Downloads (Pure)

Abstract

This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.
Original languageEnglish
Pages (from-to)60-74
Number of pages15
JournalEuropean Journal of Operational Research
Volume262
Issue number1
Early online date7 Mar 2017
DOIs
Publication statusPublished - 1 Oct 2017

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