SVR with Chaotic Genetic Algorithm in Taiwanese 3G Phone Demand Forecasting
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.
KeywordsChaotic genetic algorithm (CGA) support vector regression (SVR) third generation (3G) phones demand
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