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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

A. L. D. Loureiro (Autor) Outra; Vera L. Miguéis (Autor) FEUP; Lucas F. M. da Silva (Autor) FEUP

Revista

Título: Decision Support Systems

Vol. 114

Páginas: 81-93

ISSN: 0167-9236

Editora: Elsevier

Indexação

ISI Web of Knowledge - 104 Citações

Scopus - 138 Citações

Outras Informações

ID Authenticus: P-00P-JZV

DOI: 10.1016/j.dss.2018.08.010

Abstract (EN): 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. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13