Demand forecasting of perishable farm products using support vector machine

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

34 Scopus Citations
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Author(s)

  • Xiao Fang Du
  • Stephen C.H. Leung
  • Jin Long Zhang
  • K. K. Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)556-567
Journal / PublicationInternational Journal of Systems Science
Volume44
Issue number3
Publication statusPublished - 1 Mar 2013

Abstract

This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine (SVM) method. Since SVMs have greater generalisation performance and guarantee global minima for given training data, it is believed that support vector regression will perform well for forecasting demand for perishable farm products. In order to improve forecasting precision (FP), this article quantifies the factors affecting the sales forecast of perishable farm products based on the fuzzy theory, which is suitable for real situations. Numerical experiments show that forecasting systems with SVMs and fuzzy theory outperform the radial basis function neural network, based on the criteria of day absolute error, relative mean error and FP. Since there is no structured way to choose the free parameters of SVMs, the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article. Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting. © 2013 Copyright Taylor and Francis Group, LLC.

Research Area(s)

  • Demand forecasting, Fuzzy theory, Kernel function, Perishable farm products, Support vector machine

Citation Format(s)

Demand forecasting of perishable farm products using support vector machine. / Du, Xiao Fang; Leung, Stephen C.H.; Zhang, Jin Long; Lai, K. K.

In: International Journal of Systems Science, Vol. 44, No. 3, 01.03.2013, p. 556-567.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review