Forecasting with temporal hierarchies

Athanasopoulos, George and Hyndman, Rob J. and Kourentzes, Nikolaos and Petropoulos, Fotios (2017) Forecasting with temporal hierarchies. European Journal of Operational Research, 262 (1). pp. 60-74. ISSN 0377-2217

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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.

Item Type: Journal Article
Journal or Publication Title: European Journal of Operational Research
Additional Information: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 262, (1), 2017 DOI: 10.1016/j.ejor.2017.02.046
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
Departments: Lancaster University Management School > Management Science
ID Code: 84905
Deposited By: ep_importer_pure
Deposited On: 27 Feb 2017 16:26
Refereed?: Yes
Published?: Published
Last Modified: 28 Feb 2020 03:27
URI: https://eprints.lancs.ac.uk/id/eprint/84905

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