Deep Learning based Forecasting: a case study from the online fashion industry
This work addresses forecasting challenges for the online fashion industry, but it is incremental as it builds on existing deep learning methods with specific adaptations.
The paper tackled demand forecasting in online fashion by addressing the fixed inventory assumption through price-demand control, achieving effective results as demonstrated in their case study.
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.