Tourism demand forecasting by support vector regression and genetic algorithm

Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009

Volumn , Issue , 2009, Pages 144-146

  Cai, Zhong Jian ^a   Lu, Sheng ^a   Zhang, Xiao Bin ^b  

^a CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^b Guangxi Special Equipment Supervision and Inspection Institute   (China)

Author keywords

Auto adaptive parameters; Neural networks; Support vector regression; Tourism demand

Indexed keywords

AUTO-ADAPTIVE PARAMETERS; BACK-PROPAGATION NEURAL NETWORKS; MEAN ABSOLUTE PERCENTAGE ERROR; OPTIMAL PARAMETER; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; TOURISM DEMAND;

COMPUTER SCIENCE; FORECASTING; GENETIC ALGORITHMS; INFORMATION TECHNOLOGY; NEURAL NETWORKS; OPTIMIZATION; VECTORS;

REGRESSION ANALYSIS;

EID: 70449085050     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSIT.2009.5234447     Document Type: Conference Paper

References (5)

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3. R. Law, "Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting", Tourism Management,2000,vol.21, pp.331-340.
4. S.F. Fang, M.P. Wang, W.H. Qi, F. Zheng, "Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials", Computational Materials Science,2008,vol.44, pp. 647-655.
5. Shian-Chang Huang, Tung-Kuang Wu, "Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting", Expert Systems with Applications, 2008, vol.35, pp. 2080-2088.