SVR with hybrid chaotic genetic algorithms for tourism demand forecasting

Authors:
Wei-Chiang Hong;Yucheng Dong;Li-Yueh Chen;Shih-Yung Wei
Affiliations:
Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, Taipei 220, Taiwan;Department of Management Science, School of Management, Xi'an Jiaotong University, Xi'an 710049, P.R. China;Department of Hospitality Management, MingDao University, 369 Wen-Hua Rd., Peetow, Changhua, 52345, Taiwan;Department & Graduate Institute of Finance, National Yunlin University of Science & Technology, 123 Sec. 3, University Rd, Douliou, Yunlin, 640, Taiwan
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Accurate tourist demand forecasting systems are essential in tourism planning, particularly in tourism-based countries. Artificial neural networks are attracting attention to forecast tourism demands due to their general non-linear mapping capabilities. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training error. This investigation presents a SVR model with chaotic genetic algorithm (CGA), namely SVRCGA, to forecast the tourism demands. With the increase of the complexity and the larger problem scale of tourism demands, genetic algorithms (GAs) are often faced with the problems of premature convergence, slowly reaching the global optimal solution or trapping into a local optimum. The proposed CGA based on the chaos optimization algorithm and GAs, which employs internal randomness of chaos iterations, is used to overcome premature local optimum in determining three parameters of a SVR model. Empirical results that involve tourism demands data from existed paper reveal the proposed SVRCGA model outperforms other approaches in the literature.