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

fYear

2009

fDate

8-11 Aug. 2009

Firstpage

144

Lastpage

146

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICCSIT.2009.5234447

Filename

5234447