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  • PostScipt_Retail Forecasting: Research and Practice

    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, 38, 4, 2022 DOI: 10.1016/j.ijforecast.2021.09.012

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

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Post-script - Retail forecasting: Research and Practice

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Post-script - Retail forecasting: Research and Practice. / Fildes, Robert; Kolassa, Stephan; Ma, Shaohui.
In: International Journal of Forecasting, Vol. 38, No. 4, 31.10.2022, p. 1319-1324.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fildes, R, Kolassa, S & Ma, S 2022, 'Post-script - Retail forecasting: Research and Practice', International Journal of Forecasting, vol. 38, no. 4, pp. 1319-1324. https://doi.org/10.1016/j.ijforecast.2021.09.012

APA

Fildes, R., Kolassa, S., & Ma, S. (2022). Post-script - Retail forecasting: Research and Practice. International Journal of Forecasting, 38(4), 1319-1324. https://doi.org/10.1016/j.ijforecast.2021.09.012

Vancouver

Fildes R, Kolassa S, Ma S. Post-script - Retail forecasting: Research and Practice. International Journal of Forecasting. 2022 Oct 31;38(4):1319-1324. Epub 2021 Nov 17. doi: 10.1016/j.ijforecast.2021.09.012

Author

Fildes, Robert ; Kolassa, Stephan ; Ma, Shaohui. / Post-script - Retail forecasting : Research and Practice. In: International Journal of Forecasting. 2022 ; Vol. 38, No. 4. pp. 1319-1324.

Bibtex

@article{9f1eb4f727964560b1396970399a09b8,
title = "Post-script - Retail forecasting: Research and Practice",
abstract = "This note updates the 2019 review article “Retail forecasting: Research and Practice” in the context of the COVID-19 pandemic and the substantial new research on machine learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.",
keywords = "COVID-19, Disruption, Structural change, Instability, Omni-retailing, Online retail, Machine learning",
author = "Robert Fildes and Stephan Kolassa and Shaohui Ma",
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, 38, 4, 2022 DOI: 10.1016/j.ijforecast.2021.09.012",
year = "2022",
month = oct,
day = "31",
doi = "10.1016/j.ijforecast.2021.09.012",
language = "English",
volume = "38",
pages = "1319--1324",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "4",

}

RIS

TY - JOUR

T1 - Post-script - Retail forecasting

T2 - Research and Practice

AU - Fildes, Robert

AU - Kolassa, Stephan

AU - Ma, Shaohui

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, 38, 4, 2022 DOI: 10.1016/j.ijforecast.2021.09.012

PY - 2022/10/31

Y1 - 2022/10/31

N2 - This note updates the 2019 review article “Retail forecasting: Research and Practice” in the context of the COVID-19 pandemic and the substantial new research on machine learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

AB - This note updates the 2019 review article “Retail forecasting: Research and Practice” in the context of the COVID-19 pandemic and the substantial new research on machine learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

KW - COVID-19

KW - Disruption

KW - Structural change

KW - Instability

KW - Omni-retailing

KW - Online retail

KW - Machine learning

U2 - 10.1016/j.ijforecast.2021.09.012

DO - 10.1016/j.ijforecast.2021.09.012

M3 - Journal article

VL - 38

SP - 1319

EP - 1324

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -