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

In: Forecasting with Artificial Intelligence

Author

Listed:
  • Manuel Kunz

    (Zalando SE)

  • Stefan Birr

    (Zalando SE)

  • Mones Raslan

    (Zalando SE)

  • Lei Ma

    (Zalando SE)

  • Tim Januschowski

    (Zalando SE)

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.

Suggested Citation

  • Manuel Kunz & Stefan Birr & Mones Raslan & Lei Ma & Tim Januschowski, 2023. "Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 279-311, Palgrave Macmillan.
    • Handle: RePEc:pal:paiecp:978-3-031-35879-1_11
    DOI: 10.1007/978-3-031-35879-1_11
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