Tourism demand forecasting using novel hybrid system
Expert Systems with ApplicationsPublished 8 December 2013
Ping‐Feng Pai, Kuo-Chen Hung, Kuo-Ping Lin
Citations107
SJR quartileQ1
SJR score1.85
SNIP2.55
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TL;DR
A novel forecasting system for accurately forecasting tourism demand that combines fuzzy c-means with logarithm least-squares support vector regression technologies and demonstrates a superior performance to other methods in terms of forecasting accuracy is developed.
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.
Keywords
Social SciencesDecision SciencesBusiness, Management and Accounting
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