Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/54707
Title: Applications of evolutionary neural networks for sales forecasting of fashionable products
Authors: Yu, Y
Choi, TM 
Au, KF
Sun, ZL
Issue Date: 2010
Publisher: Information Science Reference
Source: In ES Olivas, JDM Guerrero, M Martinez-Sober, JR Magdalena-Benedito & AJ Serrano-López (Eds.), Handbook of research on machine learning applications and trends : algorithms, methods, and techniques, p. 387-403. Hershey, PA: Information Science Reference, 2010 How to cite?
Abstract: The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN ap- proach with a direct binary representation to every single neural network connection is proposed in this chapter for sales forecasting of fashionable products. In this chapter, the authors will first explore the details on how an evolutionary computation approach can be applied in searching for a desirable network structure for establishing the appropriate sales forecasting system. The optimized ENN struc- ture for sales forecasting is then developed. With the use of real sales data, the authors compare the performances of the proposed ENN forecasting scheme with several traditional methods which include artificial neural network ( ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outperforms other methods. Insights regarding the applications of ENN for forecasting sales of fashionable products are generated. Finally, future research directions are outlined.
URI: http://hdl.handle.net/10397/54707
ISBN: 9781605667669 (hardcover)
1605667668 (hardcover)
9781605667676 (electronic bk.)
1605667676 (electronic bk.)
DOI: 10.4018/978-1-60566-766-9.ch018
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