Taiwanese 3G mobile phone demand forecasting by SVR with hybrid evolutionary algorithms

Keywords: Demand forecasting Genetic algorithm–simulated annealing (GA–SA) Support vector regression (SVR) Autoregressive integrated moving average (ARIMA) General regression neural networks (GRNN) Third generation (3G) mobile phone a b s t r a c t Taiwan is one of the countries with higher mobile phone penetration rate in the world, along with the increasing maturity of 3G relevant products, the establishments of base stations, and updating regulations of 3G mobile phones, 3G mobile phones are gradually replacing 2G phones as the mainstream product. Therefore, accurate 3G mobile phones demand forecasting is desirable and necessary to communications policy makers and all enterprises. Due to the complex market competitions and various subscribers' demands, 3G mobile phones demand forecasting reveals highly non-linear characteristics. Recently, support vector regression (SVR) has been successfully employed to solve non-linear regression and time-series problems. This investigation employs genetic algorithm–simulated annealing hybrid algorithm (GA–SA) to choose the suitable parameter combination for a SVR model. Subsequently, examples of 3G mobile phones demand data from Taiwan were used to illustrate the proposed SVRGA–SA model. The empirical results reveal that the proposed model outperforms the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model.