Post-script—Retail forecasting: Research and practice
Robert Fildes,
Stephan Kolassa and
Shaohui Ma
International Journal of Forecasting, 2022, vol. 38, issue 4, 1319-1324
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 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1319-1324
DOI: 10.1016/j.ijforecast.2021.09.012
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