Table IV

The most important and highest-quality articles using support vector machines for tourism and passenger demand forecasting

AuthorsPurposeDemand type and periodDeterminantsModeling
Pai and Hong (2005) Predicting the tourism demandAnnual number of visitorsService price/Foreign exchange rate/Population/Market expenses/Gross domestic expenditure/Average hotel rateBack-propagation neural networks/Multifactor support vector machine model
Samsudin et al. (2010) Forecasting the tourism demand using a hybrid algorithmNumber of visitors per monthHistoric monthly tourist arrivals dataGroup method of data handling/Least squares support vector machine
Hong et al. (2011) Forecasting the tourism demand using a hybrid algorithmAnnual tourist arrivalsHistoric annual tourist arrivals dataSupport vector regression/Chaotic genetic algorithm
Lin and Lee (2013) Forecasting the tourism demand using a hybrid algorithmMonthly tourist arrivalsAverage hotel price/Number of hotel rooms/Capacity of international flights/GDP/CPI/Foreign exchange rateMultivariate adaptive regression splines/ANN/Support vector regression
Rafidah et al. (2017) Forecasting the tourist arrivals to Malaysia from SingaporeMonthly tourist arrivalsHistoric monthly tourist arrivals dataSupport vector machine model
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