Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry

Manuel Kunz (), Stefan Birr (), Mones Raslan (), Lei Ma () and Tim Januschowski ()
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Manuel Kunz: Zalando SE
Stefan Birr: Zalando SE
Mones Raslan: Zalando SE
Lei Ma: Zalando SE
Tim Januschowski: Zalando SE

Chapter Chapter 11 in Forecasting with Artificial Intelligence, 2023, pp 279-311 from Palgrave Macmillan

Abstract: Abstract Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry’s set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalogue and the fixed inventory assumption. While standard deep learning forecasting approachesForecasting approach cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:pal:paiecp:978-3-031-35879-1_11

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DOI: 10.1007/978-3-031-35879-1_11

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