%0 Generic %1 kunz2023learning %A Kunz, Manuel %A Birr, Stefan %A Raslan, Mones %A Ma, Lei %A Li, Zhen %A Gouttes, Adele %A Koren, Mateusz %A Naghibi, Tofigh %A Stephan, Johannes %A Bulycheva, Mariia %A Grzeschik, Matthias %A Kekić, Armin %A Narodovitch, Michael %A Rasul, Kashif %A Sieber, Julian %A Januschowski, Tim %D 2023 %K time-series %T Deep Learning based Forecasting: a case study from the online fashion industry %U http://arxiv.org/abs/2305.14406 %X 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.

@misc{kunz2023learning, 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.}, added-at = {2023-08-21T21:58:09.000+0200}, author = {Kunz, Manuel and Birr, Stefan and Raslan, Mones and Ma, Lei and Li, Zhen and Gouttes, Adele and Koren, Mateusz and Naghibi, Tofigh and Stephan, Johannes and Bulycheva, Mariia and Grzeschik, Matthias and Kekić, Armin and Narodovitch, Michael and Rasul, Kashif and Sieber, Julian and Januschowski, Tim}, biburl = {https://www.bibsonomy.org/bibtex/2ce87ad7c8f45109063cd76fd9195331f/vincentqb}, description = {Deep Learning based Forecasting: a case study from the online fashion industry}, interhash = {ac7e8df177dfb07211f90b14054e97cb}, intrahash = {ce87ad7c8f45109063cd76fd9195331f}, keywords = {time-series}, note = {cite arxiv:2305.14406}, timestamp = {2023-08-21T21:58:09.000+0200}, title = {Deep Learning based Forecasting: a case study from the online fashion industry}, url = {http://arxiv.org/abs/2305.14406}, year = 2023 }