Exploring the use of deep neural networks for sales forecasting in fashion retail

作者：

Highlights：

• New fashion products' sales are predicted using data mining regression techniques.

• The performance of both deep neural networks and shallow methods is explored.

• Expert knowledge is part of the predictive variables of the developed models.

• Variables describing physical and distribution characteristics are considered.

摘要

In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company.

论文关键词：Sales forecasting,Fashion retail,Support vector regression,Artificial neural networks,Deep neural networks

论文评审过程：Received 31 January 2018, Revised 22 August 2018, Accepted 22 August 2018, Available online 29 August 2018, Version of Record 4 September 2018.

论文官网地址：https://doi.org/10.1016/j.dss.2018.08.010