Fashion retail forecasting by evolutionary neural networks

Kin Fan Au, Tsan Ming Choi, Yong Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

99 Citations (Scopus)

Abstract

Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years' apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We find that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce short-term retail forecasting for apparels, which share these features.
Original languageEnglish
Pages (from-to)615-630
Number of pages16
JournalInternational Journal of Production Economics
Volume114
Issue number2
DOIs
Publication statusPublished - 1 Aug 2008

Keywords

  • Evolutionary neural networks
  • Forecasting
  • SARIMA

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this