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2024 | OriginalPaper | Buchkapitel

Forecasting and Planning with Auxiliary Data During a Pandemic: Evidence from U.K. Google Trends

verfasst von : Maryam Mojdehi, Konstantinos Nikolopoulos, Vasileios Bougioukos

Erschienen in: Impacts of COVID-19 on Supply Chains

Verlag: Springer Nature Switzerland

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Abstract

In this chapter, we develop reliable methods to predict excess demand during the COVID-19 and similar pandemics. Weekly Google Trend data is gathered for nine retail products in three different categories electronics, toiletries, and groceries. Forward Stepwise Selection is used to investigate the relationship between excess demand and multiple other variables at once. Finally, various models based on Regression, Decision Trees, and Neural Networks are developed for demand prediction. The findings are that forecasting during a pandemic was less challenging for electronics than for toiletries and groceries. Consumer demand is affected by the public health environment of a pandemic, and consumers’ panic buying and hoarding had a serious impact on demand for those products for which they had most immediate need, such as toiletries and groceries.

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Titel: Forecasting and Planning with Auxiliary Data During a Pandemic: Evidence from U.K. Google Trends
verfasst von: Maryam Mojdehi
Konstantinos Nikolopoulos
Vasileios Bougioukos
Verlag: Springer Nature Switzerland
Buch: Impacts of COVID-19 on Supply Chains
Print ISBN: 978-3-031-47733-1

Electronic ISBN: 978-3-031-47734-8

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-47734-8_3

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