A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data
- https://doi.org/10.2991/ijcis.d.191122.002How to use a DOI?
- Sales forecasting; Siamese neural networks; Fashion products; Fashion retail
In the fashion market, the lack of historical sales data for new products imposes the use of methods based on Stock Keeping Unit (SKU) attributes. Recent works suggest the use of functional data analysis to assign the most accurate sales profiles to each item. An application of siamese neural networks is proposed to perform long-term sales forecasting for new products. A comparative study using benchmark models is conducted on data from a European fashion retailer. This shows that the proposed application can produce valuable item level sales forecasts.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Giuseppe Craparotta AU - Sébastien Thomassey AU - Amedeo Biolatti PY - 2019 DA - 2019/11/29 TI - A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data JO - International Journal of Computational Intelligence Systems SP - 1537 EP - 1546 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191122.002 DO - https://doi.org/10.2991/ijcis.d.191122.002 ID - Craparotta2019 ER -