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

2023/05/23 by Manuel Kunz, Kunz, Manuel, Stefan Birr +31 · 1 voice · 1 citation
Decision Sciences · #Advanced Statistical Process Monitoring #Forecasting Techniques and Applications #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.2305.14406

openalex publication_date 2023/05/23 · openalex created_date 2023/05/27 · openalex updated_date 2026/07/28

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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Discussions

• Yes we do. An earlier version of our base demand estimation is explained here arxiv.org/pdf/2305.14406 [bsky, 15 points, 3 comments]

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