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

Abstract__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.