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Tourism demand forecasting using novel hybrid system
Affiliation:1. Department of Information Management, National Chi Nan University, 1, University Rd., Puli, Nantou 545, Taiwan, ROC;2. Department of Computer Science and Information Management, Hungkuang University, Taiwan, ROC;3. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan, Taiwan, ROC;1. Nagoya Institute of Technology, Department of Computer Science, Gokisho, Showa, Nagoya, Aichi, 466-8555, Japan;2. University of the Ryukyus, Department of Electrical Engineering, Nakagami, Nishihara, Okinawa, 903-0213, Japan;1. Department of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo, Rua do Matao, 1010, Cidade Universitaria, CEP 05508-090 Sao Paulo, SP, Brazil;2. Computing Institute, Federal University of Alagoas, Campus A.C. Simoes, BR 104, Norte, km 97, Cidade Universitaria, CEP 57072-970 Maceio, AL, Brazil;3. Department of Computer Systems, Institute of Mathematics and Computional Sciences, University of Sao Paulo, Avenida Trabalhador Sao-carlense, 400 Centro, CEP 13566-590 Sao Carlos, SP, Brazil;1. Department of Computer Languages and Systems, University of Seville, Av Reina Mercedes S/N, 41012 Seville, Spain;2. School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB7 7NU, United Kingdom;1. Center of Informatics, CIn, Federal University of Pernambuco, UFPE, Hélio Ramos Av., 50740560 Recife, Brazil;2. Philips Research, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA;3. Intel Corporation, 2111 NE 25th Avenue, Hillsboro, OR 97124, USA;1. School of Software, Northeastern University, Shenyang 110819, China;2. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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:Forecasting  Tourism demand  Least-squares support vector regression  Genetic algorithms
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