Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25018
Title: Fashion retail forecasting by evolutionary neural networks
Authors: Au, KF
Choi, TM 
Yu, Y
Keywords: Evolutionary neural networks
Forecasting
SARIMA
Issue Date: 2008
Publisher: Elsevier
Source: International journal of production economics, 2008, v. 114, no. 2, p. 615-630 How to cite?
Journal: International journal of production economics 
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.
URI: http://hdl.handle.net/10397/25018
ISSN: 0925-5273
DOI: 10.1016/j.ijpe.2007.06.013
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