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  • Kourentzes 2020 Elucidate_structure_in_intermittent_demand_series

    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, 288, 1, 2021 DOI: 10.1016/j.ejor.2020.05.046

    Accepted author manuscript, 1.06 MB, PDF document

    Embargo ends: 30/05/22

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Elucidate structure in intermittent demand series

Research output: Contribution to journalJournal articlepeer-review

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Elucidate structure in intermittent demand series. / Kourentzes, Nikolaos; Athanasopoulos, George.

In: European Journal of Operational Research, Vol. 288, No. 1, 01.01.2021, p. 141-152.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Kourentzes, N & Athanasopoulos, G 2021, 'Elucidate structure in intermittent demand series', European Journal of Operational Research, vol. 288, no. 1, pp. 141-152. https://doi.org/10.1016/j.ejor.2020.05.046

APA

Kourentzes, N., & Athanasopoulos, G. (2021). Elucidate structure in intermittent demand series. European Journal of Operational Research, 288(1), 141-152. https://doi.org/10.1016/j.ejor.2020.05.046

Vancouver

Kourentzes N, Athanasopoulos G. Elucidate structure in intermittent demand series. European Journal of Operational Research. 2021 Jan 1;288(1):141-152. https://doi.org/10.1016/j.ejor.2020.05.046

Author

Kourentzes, Nikolaos ; Athanasopoulos, George. / Elucidate structure in intermittent demand series. In: European Journal of Operational Research. 2021 ; Vol. 288, No. 1. pp. 141-152.

Bibtex

@article{5cdc7a59b2ea46179dffd261389f9ebb,
title = "Elucidate structure in intermittent demand series",
abstract = "Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.",
keywords = "Forecasting, temporal aggregation, temporal hierarchies, forecast combination, forecast reconciliation",
author = "Nikolaos Kourentzes and George Athanasopoulos",
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, 288, 1, 2021 DOI: 10.1016/j.ejor.2020.05.046",
year = "2021",
month = jan,
day = "1",
doi = "10.1016/j.ejor.2020.05.046",
language = "English",
volume = "288",
pages = "141--152",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Elucidate structure in intermittent demand series

AU - Kourentzes, Nikolaos

AU - Athanasopoulos, George

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, 288, 1, 2021 DOI: 10.1016/j.ejor.2020.05.046

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

AB - Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

KW - Forecasting

KW - temporal aggregation

KW - temporal hierarchies

KW - forecast combination

KW - forecast reconciliation

U2 - 10.1016/j.ejor.2020.05.046

DO - 10.1016/j.ejor.2020.05.046

M3 - Journal article

VL - 288

SP - 141

EP - 152

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -