English

Abstract

This study attempts to forecast tourist inflow in South East Asia and choses Singapore as a case. For Singapore, tourism is one of the major sources of foreign exchange earnings since it has no natural resources to support its economy. Therefore, forecasting of tourist arrivals in the country becomes very important for the reason that the forecasting may help tourism related service industries (e.g. airlines, hotels, shopping malls, transporters and catering services, etc.) to plan and prepare their resources and activities in an optimal way. In this paper, seasonal autoregressive integrated moving average (SARIMA) methodology was considered for making monthly predictions on tourist arrival in Singapore. The best model for forecasting is found out to be (1,0,1)(1,1,0) 12 and monthly forecasting were obtained for two years in future. Further, various statistical tests (e.g. Dickey Fuller, KPSS, HEGY, Ljung-Box, Box-Pierce etc.) were applied on the time series for adequacy of best model to fit, residual autocorrelation analysis and for the accuracy of the prediction.