Forecasting the Chinese tourist arrivals to Thailand the time series approach
© Medwell Journals, 2016. The ARIMA Model is good for tourism demand forecasting when the uncertainty is low. However, when several uncertainty events happened, such as Chinese holidays, political turmoil and structural changes in our study, the model reacts very weakly. After comparing the out-of-s...
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2017
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th-cmuir.6653943832-424242017-09-28T04:26:59Z Forecasting the Chinese tourist arrivals to Thailand the time series approach Gong X. Songsak Sriboonchitta Kuson S. © Medwell Journals, 2016. The ARIMA Model is good for tourism demand forecasting when the uncertainty is low. However, when several uncertainty events happened, such as Chinese holidays, political turmoil and structural changes in our study, the model reacts very weakly. After comparing the out-of-sample forecast performances of ARIMA and Seasonal ARIMA (SARIMA) Models, we suggest that the SARIMA Model produce a more stable forecast especially when the structural change occurs and high uncertainty appears. We recommend the policy makers and relevant travel decision section to use SARIMA method to conduct the tourist forecasting. 2017-09-28T04:26:59Z 2017-09-28T04:26:59Z 2016-01-01 Journal 18185800 2-s2.0-85005950760 10.3923/sscience.2016.4617.4621 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005950760&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42424 |
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Chiang Mai University Library |
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Thailand |
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© Medwell Journals, 2016. The ARIMA Model is good for tourism demand forecasting when the uncertainty is low. However, when several uncertainty events happened, such as Chinese holidays, political turmoil and structural changes in our study, the model reacts very weakly. After comparing the out-of-sample forecast performances of ARIMA and Seasonal ARIMA (SARIMA) Models, we suggest that the SARIMA Model produce a more stable forecast especially when the structural change occurs and high uncertainty appears. We recommend the policy makers and relevant travel decision section to use SARIMA method to conduct the tourist forecasting. |
| format |
Journal |
| author |
Gong X. Songsak Sriboonchitta Kuson S. |
| spellingShingle |
Gong X. Songsak Sriboonchitta Kuson S. Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| author_facet |
Gong X. Songsak Sriboonchitta Kuson S. |
| author_sort |
Gong X. |
| title |
Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| title_short |
Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| title_full |
Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| title_fullStr |
Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| title_full_unstemmed |
Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| title_sort |
forecasting the chinese tourist arrivals to thailand the time series approach |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005950760&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42424 |
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