Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation
Tourism is one of the key service industries in Thailand, with a 5.27% share of Gross Domestic Product in 2003. Since 2000, international tourist arrivals, particularly those from East Asia, to Thailand have been on a continuous upward trend. Tourism forecasts can be made based on previous observati...
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th-cmuir.6653943832-490242018-08-16T02:13:27Z Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation Chia Lin Chang Songsak Sriboonchitta Aree Wiboonpongse Computer Science Mathematics Tourism is one of the key service industries in Thailand, with a 5.27% share of Gross Domestic Product in 2003. Since 2000, international tourist arrivals, particularly those from East Asia, to Thailand have been on a continuous upward trend. Tourism forecasts can be made based on previous observations, so that historical analysis of tourist arrivals can provide a useful understanding of inbound trips and the behaviour of trends in foreign tourist arrivals to Thailand. As tourism is seasonal, a good forecast is required for stakeholders in the industry to manage risk. Previous research on tourism forecasts has typically been based on annual and monthly data analysis, while few past empirical tourism studies using the Box-Jenkins approach have taken account of pre-testing for seasonal unit roots based on Franses [P.H. Franses, Seasonality, nonstationarity and the forecasting of monthly time series, International Journal of Forecasting 7 (1991) 199-208] and Beaulieu and Miron [J.J. Beaulieu, J.A. Miron, Seasonal unit roots in aggregate U.S. data, Journal of Econometrics 55 (1993) 305-328] framework. An analysis of the time series of tourism demand, specifically monthly tourist arrivals from six major countries in East Asia to Thailand, from January 1971 to December 2005 is examined. This paper analyses stationary and non-stationary tourist arrivals series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box-Jenkins autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models are estimated, with the tourist arrivals series showing seasonal patterns. The fitted ARIMA and seasonal ARIMA models forecast tourist arrivals from East Asia very well for the period 2006(1)-2008(1). Total monthly and annual forecasts can be obtained through temporal and spatial aggregation. © 2008 IMACS. 2018-08-16T02:08:29Z 2018-08-16T02:08:29Z 2009-01-01 Journal 03784754 2-s2.0-57649088127 10.1016/j.matcom.2008.09.006 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=57649088127&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49024 |
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Computer Science Mathematics Chia Lin Chang Songsak Sriboonchitta Aree Wiboonpongse Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
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Tourism is one of the key service industries in Thailand, with a 5.27% share of Gross Domestic Product in 2003. Since 2000, international tourist arrivals, particularly those from East Asia, to Thailand have been on a continuous upward trend. Tourism forecasts can be made based on previous observations, so that historical analysis of tourist arrivals can provide a useful understanding of inbound trips and the behaviour of trends in foreign tourist arrivals to Thailand. As tourism is seasonal, a good forecast is required for stakeholders in the industry to manage risk. Previous research on tourism forecasts has typically been based on annual and monthly data analysis, while few past empirical tourism studies using the Box-Jenkins approach have taken account of pre-testing for seasonal unit roots based on Franses [P.H. Franses, Seasonality, nonstationarity and the forecasting of monthly time series, International Journal of Forecasting 7 (1991) 199-208] and Beaulieu and Miron [J.J. Beaulieu, J.A. Miron, Seasonal unit roots in aggregate U.S. data, Journal of Econometrics 55 (1993) 305-328] framework. An analysis of the time series of tourism demand, specifically monthly tourist arrivals from six major countries in East Asia to Thailand, from January 1971 to December 2005 is examined. This paper analyses stationary and non-stationary tourist arrivals series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box-Jenkins autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models are estimated, with the tourist arrivals series showing seasonal patterns. The fitted ARIMA and seasonal ARIMA models forecast tourist arrivals from East Asia very well for the period 2006(1)-2008(1). Total monthly and annual forecasts can be obtained through temporal and spatial aggregation. © 2008 IMACS. |
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Chia Lin Chang Songsak Sriboonchitta Aree Wiboonpongse |
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Chia Lin Chang Songsak Sriboonchitta Aree Wiboonpongse |
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Chia Lin Chang |
title |
Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
title_short |
Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
title_full |
Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
title_fullStr |
Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
title_full_unstemmed |
Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation |
title_sort |
modelling and forecasting tourism from east asia to thailand under temporal and spatial aggregation |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=57649088127&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49024 |
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