Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bergstrand JH (1991) Structural determinants of real exchange rates and national price levels: some empirical evidence. Am Econ Rev 81:325–334
Blake A, Durbarry R, Eugenio-Martin JL, Gooroochurn N, Hay B, Lennon J, Sinclair MT, Sugiyarto G, Yeoman I (2006) Integrating forecasting and CGE models: the case of tourism in Scotland. Tourism Manage 27:292–305
Cho V (2003) A comparison of three different approaches to tourist arrival forecasting. Tourism Manage 24:323–330
Clague C (1986) Determinants of national price level: some empire results. Rev Economics and Statics 68:320–323
De Mello, Pack A, Sinclair MT (2002) A system of equations models of UK tourism demand in neighbouring countries. Taylor and Francis J 34:509–521
Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183–192
Hao J, Var T, Chon J (2003) A forecasting model of tourist arrivals from major markets to Thailand. Tourism Anal 8(1):33–45
Hong Kong Tourism Board (2005) A statistical review of Hong Kong tourism 2004
Hornik K (1993) Some new results on neural network approximation. Neural Netw 6:1069–1072
Huarng KH, Moutinho L, Yu THK (2007) An advanced approach to forecasting tourism demand in Taiwan. J Travel Tourism Mark 21(4):15–24
Indro DC, Jiang CX, Patuwo BE, Zhang GP (1999). Predicting mutual fund performance using artificial neural networks. Omega Int J Manage Sci 27:373–380
Kravis IB, Lipsey RE (1983) Toward an explanation of national price level (Princeton Studies in International Finance No. 52), Princeton University, Princeton, NJ
Kravis IB, Lipsey RE (1987) The assessment of national price levels. In: Arndt SW, Richardson JD (eds) Real-financial linkages among open economies. MIT Press, Cambridge, MA, pp 97–134
Kravis IB, Lipsey RE (1988) National price levels and the prices of tradeables and nontradeables. Am Econ Rev 78:474–478
Kulendran N, Witt SF (2003) Leading indicator tourism forecasts. Tourism Manage 24:503–510
Law R (2000) Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Manage 21:331–340
Law R, Au N (1999) A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Manage 20(1):89–97
Lindberg K, Aylward B (1999) Price responsiveness in the developing country nature tourism context: review and Costa Rican case study. J Leisure Res 31:281–299
Maddison D (2001) In search of warmer climates? The impact of climate change on flow of British tourists. Clim Change 49(1–2):193–208
Muzaffer U (2004) Advancement in computing: implications for tourism and hospitality. Scand J Hosp Tour 4(3):208–224
Oh C-O, Morzuch BJ (2005) Evaluating time-series models to forecast the demand for tourism in Singapore: comparing within-sample and postsample result. J Travel Res 43:404–413
Pai PF, Hong WC, Hong PT, Chen CT (2006) The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. Int J Manage 23:375–385
Papatheodorou A (1999) The demand for international tourism in the Mediterranean region. Appl Econ 31:619–630
Samuel SK, Yingzhi G, Jerome A (2005) Preference and positioning analyses of overseas destinations by Mainland Chinese outbound pleasure tourists. J Travel Res 44:212–220
Smeral E (2003) A structural view of tourism growth. Tourism Econ 9(1):77–93
Smith V, Kopp R (1980) The spatial limits of the travel cost recreational demand model. Land Econ 56:64–71
Smith KA, Gupta JND (2002) Neural networks in business: techniques and applications. Idea Hornik Group Publishing, Hershey, PA
Song H, Witt SF (2003) Tourism forecasting: the general-to-specific approach. J Travel Res 42:65–74
Song H, Witt SF (2006) Forecasting international tourist flows to Macau. Tourism Manage 27:214–224
Song H, Wong KKF (2003) Tourism demand modeling: a time-varying parameter approach. J Travel Res 42(1):57–64
Song H, Witt SF, Jensen TC (2003) Tourism forecasting: accuracy of alternative econometric models. Int J Forecast 19:123–141
Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold, New York
Widrow B, Rumelhart DE, Lehr MA (1994) Neural networks: applications in industry, business and science. Commun ACM 37(3):93–105
Wong KKF, Song H, Chon KS (2006) Bayesian models for tourism demand forecasting. Tourism Manage 27:773–780
World Tourism Organization (1999) Tourism: 2020 vision-executive summary. World Tourism Organization, Madrid, Spain
Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Moutinho, L., Huarng, KH., Yu, T.HK. et al. Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan. Serv Bus 2, 219–232 (2008). https://doi.org/10.1007/s11628-008-0037-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11628-008-0037-3