Deep Learning based Forecasting: a case study from the online fashion industry
Machine Learning
2023-05-25 v1 Artificial Intelligence
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.
Cite
@article{arxiv.2305.14406,
title = {Deep Learning based Forecasting: a case study from the online fashion industry},
author = {Manuel Kunz and Stefan Birr and Mones Raslan and Lei Ma and Zhen Li and Adele Gouttes and Mateusz Koren and Tofigh Naghibi and Johannes Stephan and Mariia Bulycheva and Matthias Grzeschik and Armin Kekić and Michael Narodovitch and Kashif Rasul and Julian Sieber and Tim Januschowski},
journal= {arXiv preprint arXiv:2305.14406},
year = {2023}
}