Title :
Tourism demand forecasting by support vector regression and genetic algorithm
Author :
Cai, Zhong-Jian ; Lu, Sheng ; Zhang, Xiao-Bin
Author_Institution :
Sch. of Comput. Sci. & Inf. Eng., Chongqing Technol. & Bus. Univ., Chongqing, China
Abstract :
Support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast tourism demand. Genetic algorithm (GA) is used to search for SVR´s optimal parameters, and adopt the optimal parameters to construct the SVR models. This study examines the feasibility of SVR in tourism demand forecasting by comparing it with back-propagation neural networks (BPNN).The experimental results indicate that the proposed G-SVR model outperforms the BPNN based on mean absolute percentage error (MAPE).
Keywords :
backpropagation; economic forecasting; genetic algorithms; neural nets; regression analysis; search problems; support vector machines; travel industry; BPNN; backpropagation neural network; genetic algorithm; mean absolute percentage error; search problem; support vector regression; tourism demand forecasting; Computer science; Demand forecasting; Economic forecasting; Genetic algorithms; Genetic engineering; Lagrangian functions; Neural networks; Predictive models; Risk management; Vectors; auto-adaptive parameters; neural networks; support vector regression; tourism demand;
Conference_Titel :
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4519-6
Electronic_ISBN :
978-1-4244-4520-2
DOI :
10.1109/ICCSIT.2009.5234447
