Tourism demand forecasting using novel hybrid system

Authors:
Ping-Feng Pai;Kuo-Chen Hung;Kuo-Ping Lin
Affiliations:
Department of Information Management, National Chi Nan University, 1, University Rd., Puli, Nantou 545, Taiwan, ROC;Department of Computer Science and Information Management, Hungkuang University, Taiwan, ROC;Department of Information Management, Lunghwa University of Science and Technology, Taoyuan, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

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Abstract

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.