Application of SVR with chaotic GASA algorithm to forecast Taiwanese 3G mobile phone demand

Authors:
Li-Yueh Chen
Affiliations:
-
Venue:
Neurocomputing
Year:
2014

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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. Taiwan will be the country with higher 3G phone penetration rate in the world. Therefore, accurate 3G phones demand forecasting is necessary for those communication related enterprises. Due to complicate market growth tendency and multi-variate competitions, different subscribers with different demand types, 3G phones demand forecasting is with highly nonlinear characteristics. 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 (mapped by cat function) with genetic algorithm-simulated annealing algorithm (namely CGASA) to improve the forecasting performance. The proposed SVRCGASA employs internal randomness of chaos iterations which is with better performance in function optimization to overcome premature local optimum that is suffered by GA-SA. Subsequently, a numerical example of 3G phones demand data from Taiwan are used to illustrate the proposed SVRCGASA model. The empirical results reveal that the proposed model outperforms the other three models, namely the autoregressive integrated moving average (ARIMA) model, the general regression neural networks (GRNN) model, SVRGA model, and SVRGASA model.