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  • Trapero 2018 quantile-forecast-optimal

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. 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 International Journal of Forecasting, 35, 1, 2019 DOI: 10.1016/j.ijforecast.2018.05.009

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Quantile forecast optimal combination to enhance safety stock estimation

Research output: Contribution to journalJournal article

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Quantile forecast optimal combination to enhance safety stock estimation. / Trapero Arenas, Juan Ramon; Cardos, Manuel; Kourentzes, Nikolaos.

In: International Journal of Forecasting, Vol. 35, No. 1, 01.2019, p. 239-250.

Research output: Contribution to journalJournal article

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Author

Trapero Arenas, Juan Ramon ; Cardos, Manuel ; Kourentzes, Nikolaos. / Quantile forecast optimal combination to enhance safety stock estimation. In: International Journal of Forecasting. 2019 ; Vol. 35, No. 1. pp. 239-250.

Bibtex

@article{a6506d6743674c91a633179d0a4f97b9,
title = "Quantile forecast optimal combination to enhance safety stock estimation",
abstract = "The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian iid (independently and identically distributed). However, deviations from iid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times.",
keywords = "Combination, GARCH, Kernel density estimation, Quantile forecasting, Risk, Safety stock, Supply chain, Tick loss",
author = "{Trapero Arenas}, {Juan Ramon} and Manuel Cardos and Nikolaos Kourentzes",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in International Journal of Forecasting. 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 International Journal of Forecasting, 35, 1, 2019 DOI: 10.1016/j.ijforecast.2018.05.009",
year = "2019",
month = jan
doi = "10.1016/j.ijforecast.2018.05.009",
language = "English",
volume = "35",
pages = "239--250",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Quantile forecast optimal combination to enhance safety stock estimation

AU - Trapero Arenas, Juan Ramon

AU - Cardos, Manuel

AU - Kourentzes, Nikolaos

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. 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 International Journal of Forecasting, 35, 1, 2019 DOI: 10.1016/j.ijforecast.2018.05.009

PY - 2019/1

Y1 - 2019/1

N2 - The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian iid (independently and identically distributed). However, deviations from iid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times.

AB - The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian iid (independently and identically distributed). However, deviations from iid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times.

KW - Combination

KW - GARCH

KW - Kernel density estimation

KW - Quantile forecasting

KW - Risk

KW - Safety stock

KW - Supply chain

KW - Tick loss

U2 - 10.1016/j.ijforecast.2018.05.009

DO - 10.1016/j.ijforecast.2018.05.009

M3 - Journal article

VL - 35

SP - 239

EP - 250

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 1

ER -