Applications of evolutionary neural networks for sales forecasting of fashionable products

Yong Yu, Tsan Ming Choi, Kin Fan Au, Zhan Li Sun

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

1 Citation (Scopus)

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 approach 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 structure 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.
Original languageEnglish
Title of host publicationHandbook of Research on Machine Learning Applications and Trends
Subtitle of host publicationAlgorithms, Methods, and Techniques
PublisherIGI Global
Pages387-403
Number of pages17
ISBN (Print)9781605667669
DOIs
Publication statusPublished - 1 Dec 2009

ASJC Scopus subject areas

  • Computer Science(all)

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