Deep Learning based Forecasting: a case study from the online fashion industry

• URL: http://arxiv.org/abs/2305.14406v1
• Date: Tue, 23 May 2023 13:30:35 GMT
• Title: Deep Learning based Forecasting: a case study from the online fashion industry
• Authors: Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Keki\'c, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski
• Abstract summary: We describe the data and our modelling approach for this forecasting problem in detail and present empirical results. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results.
• Score: 7.694480564850072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 catalog and the fixed inventory assumption. While standard deep learning forecasting approaches 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.

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