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SVR with Chaotic Genetic Algorithm in Taiwanese 3G Phone Demand Forecasting

  • Li-Yueh Chen
  • Wei-Chiang Hong
  • Bijaya Ketan Panigrahi
  • Shih Yung Wei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

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.

Keywords

Chaotic genetic algorithm (CGA) support vector regression (SVR) third generation (3G) phones demand 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Li-Yueh Chen
    • 1
  • Wei-Chiang Hong
    • 2
  • Bijaya Ketan Panigrahi
    • 3
  • Shih Yung Wei
    • 2
  1. 1.Department of Global Marketing and LogisticsMingDao UniversityPeetowTaiwan, R.O.C.
  2. 2.Department of Information ManagementOriental Institute of TechnologyPanchiaoTaiwan, R.O.C.
  3. 3.Department of Electrical EngineeringIndian Institutes of Technology (IITs)India

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