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  • TemporalHierarchies_2017_EJOR

    Rights statement: 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

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Forecasting with temporal hierarchies

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Forecasting with temporal hierarchies. / Athanasopoulos, George; Hyndman, Rob J.; Kourentzes, Nikolaos; Petropoulos, Fotios.

In: European Journal of Operational Research, Vol. 262, No. 1, 01.10.2017, p. 60-74.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Athanasopoulos, G, Hyndman, RJ, Kourentzes, N & Petropoulos, F 2017, 'Forecasting with temporal hierarchies', European Journal of Operational Research, vol. 262, no. 1, pp. 60-74. https://doi.org/10.1016/j.ejor.2017.02.046

APA

Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60-74. https://doi.org/10.1016/j.ejor.2017.02.046

Vancouver

Athanasopoulos G, Hyndman RJ, Kourentzes N, Petropoulos F. Forecasting with temporal hierarchies. European Journal of Operational Research. 2017 Oct 1;262(1):60-74. https://doi.org/10.1016/j.ejor.2017.02.046

Author

Athanasopoulos, George ; Hyndman, Rob J. ; Kourentzes, Nikolaos ; Petropoulos, Fotios. / Forecasting with temporal hierarchies. In: European Journal of Operational Research. 2017 ; Vol. 262, No. 1. pp. 60-74.

Bibtex

@article{1ae6e252ec654fe0acebd6b8af52cb05,
title = "Forecasting with temporal hierarchies",
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.",
keywords = "Forecasting, Hierarchical Forecasting, Temporal Aggregation, Reconciliation, Forecast Combination",
author = "George Athanasopoulos and Hyndman, {Rob J.} and Nikolaos Kourentzes and Fotios Petropoulos",
note = "This is the author{\textquoteright}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",
year = "2017",
month = oct,
day = "1",
doi = "10.1016/j.ejor.2017.02.046",
language = "English",
volume = "262",
pages = "60--74",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Forecasting with temporal hierarchies

AU - Athanasopoulos, George

AU - Hyndman, Rob J.

AU - Kourentzes, Nikolaos

AU - Petropoulos, Fotios

N1 - 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

PY - 2017/10/1

Y1 - 2017/10/1

N2 - 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.

AB - 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.

KW - Forecasting

KW - Hierarchical Forecasting

KW - Temporal Aggregation

KW - Reconciliation

KW - Forecast Combination

U2 - 10.1016/j.ejor.2017.02.046

DO - 10.1016/j.ejor.2017.02.046

M3 - Journal article

VL - 262

SP - 60

EP - 74

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -