Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan
The study of tourism demand is attracting more and more attention. Hence, it is important to understand the variables that affect tourism demand and to forecast the demand. Many studies have been conducted to analyze the demands in various countries. Recently, China has been expected to become one of the largest originators of outbound tourists in the world. Hence, it is interesting to explore what the variables are that affect the Mainland Chinese arrivals to Taiwan and to forecast its corresponding tourism demand. This study applies neural networks to select proper models, and then to forecast the demand. Copyright Springer-Verlag 2008
Volume (Year): 2 (2008)
Issue (Month): 3 (September)
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- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Maria De Mello & Alan Pack & M. Thea Sinclair, 2002. "A system of equations model of UK tourism demand in neighbouring countries," Applied Economics, Taylor & Francis Journals, vol. 34(4), pages 509-521.
- V. Kerry Smith & Raymond J. Kopp, 1980. "The Spatial Limits of the Travel Cost Recreational Demand Model," Land Economics, University of Wisconsin Press, vol. 56(1), pages 64-72.
- Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
- Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
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