Tourism demand forecasting using novel hybrid system

• Published in: Expert systems with applications Vol. 41; no. 8; pp. 3691 - 3702
• Main Authors: Pai, Ping-Feng, Hung, Kuo-Chen, Lin, Kuo-Ping
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.06.2014; Elsevier
• Subjects: Applications; Applied sciences; Computer science; control theory; systems; Data processing. List processing. Character string processing; Demand; Exact sciences and technology; Forecasting; Fuzzy; Fuzzy c-means; Genetic algorithms; Insurance, economics, finance; Least-squares support vector regression; Mathematical analysis; Mathematics; Memory organisation. Data processing; Policies; Probability and statistics; Regression; Sciences and techniques of general use; Software; Statistics; Tourism; Tourism demand; Hong Kong; Asia; China; Least-squares support vector regression; Tourism demand; Forecasting; Fuzzy c-means; Genetic algorithms; Cluster analysis; Performance evaluation; Policy; Service industries; Demand forecasting; Empirical method; Service firm; Regression analysis; Construction system; Hybrid system; Fuzzy logic; Arrival time; Tourism; Genetic algorithm; Least squares method; Center of mass; Vector support machine; Planning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.12.007

•We developed a novel hybrid system for accurately forecasting tourism demand.•The hybrid system effectively improves previous method by preprocess mechanisms.•Empirical results indicate that the proposed hybrid system has superior performance. 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.