SVR with chaotic genetic algorithm in taiwanese 3g phone demand forecasting

Authors:
Li-Yueh Chen;Wei-Chiang Hong;Bijaya Ketan Panigrahi;Shih Yung Wei
Affiliations:
Department of Global Marketing and Logistics, MingDao University, Peetow, Changhua, Taiwan, R.O.C.;Department of Information Management, Oriental Institute of Technology, Panchiao, Taipei, Taiwan, R.O.C.;Department of Electrical Engineering, Indian Institutes of Technology (IITs), India;Department of Information Management, Oriental Institute of Technology, Panchiao, Taipei, Taiwan, R.O.C.
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

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Abstract

Along with the increases of 3G relevant products and the updating regulations of 3G phones, 3G phones are gradually replacing 2G phones as the mainstream product in Taiwan. Therefore, accurate 3G phones demand forecasting is necessary for those communication related enterprises. Recently,support vector regression (SVR) has been successfully applied to solve nonlinear regression and time series problems. This investigation presents a 3G phones demand forecasting model which combines chaotic sequence with genetic algorithm to improve the forecasting performance. Subsequently, a numerical example of 3G phones demand data from Taiwan is used to illustrate the proposed SVRCGA model. The empirical results reveal that the proposed model outperforms the other three existed models, namely the autoregressive integrated moving average (ARIMA) model, the general regression neural networks (GRNN) model, and SVRGASA model.