Tourism demand forecasting by support vector regression and genetic algorithm

被引：16

作者：

Cai, Zhong-jian ^{[1
]}

Lu, Sheng ^{[1
]}

Zhang, Xiao-bin ^{[2
]}

机构：

[1] Chongqing Technol & Business Univ, Sch Comp Sci & Informat Engn, Chongqing, Peoples R China

[2] Guangxi Special Equipment Supervis & Inspect Inst, Nanning, Peoples R China

来源：

2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 5 | 2009年

关键词：

support vector regression; tourism demand; neural networks; auto-adaptive parameters;

D O I：

10.1109/ICCSIT.2009.5234447

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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).

引用

页码：144 / +

页数：2

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