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Mathematical Problems in Engineering
Volume 2014, Article ID 437898, 13 pages
http://dx.doi.org/10.1155/2014/437898
Research Article

Sales Growth Rate Forecasting Using Improved PSO and SVM

1College of Computer Science, Chongqing University, Chongqing 400030, China
2School of Software Engineering, Chongqing University, Chongqing 400030, China
3Department of Computer Science, University of Auckland, Auckland 1010, New Zealand

Received 6 March 2014; Revised 31 May 2014; Accepted 3 June 2014; Published 24 June 2014

Academic Editor: Marcelo J. Colaço

Copyright © 2014 Xibin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.