Global optimization and simulated annealing
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
Hybrid approach to the Japanese candlestick method for financial forecasting
Expert Systems with Applications: An International Journal
Speaker identification based on the frame linear predictive coding spectrum technique
Expert Systems with Applications: An International Journal
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A general regression neural network
IEEE Transactions on Neural Networks
SVR with chaotic genetic algorithm in taiwanese 3g phone demand forecasting
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Hi-index | 12.05 |
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.