Deep Learning based Forecasting: a case study from the online fashion
  industry
Manuel Kunz Stefan Birr Mones Raslan Lei Ma Zhen Li Adele Gouttes Mateusz Koren Tofigh Naghibi Johannes Stephan Mariia Bulycheva Matthias Grzeschik Armin Kekić Michael Narodovitch Kashif Rasul Julian Sieber Tim Januschowski
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.