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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th-cmuir.6653943832-563342018-09-05T03:15:05Z Forecasting the Chinese tourist arrivals to Thailand the time series approach Xue Gong Songsak Sriboonchitta Siwarat Kuson Social Sciences © 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. 2018-09-05T03:15:05Z 2018-09-05T03:15:05Z 2016-01-01 Journal 19936125 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/6653943832/56334 |
| institution |
Chiang Mai University |
| building |
Chiang Mai University Library |
| country |
Thailand |
| collection |
CMU Intellectual Repository |
| topic |
Social Sciences |
| spellingShingle |
Social Sciences Xue Gong Songsak Sriboonchitta Siwarat Kuson Forecasting the Chinese tourist arrivals to Thailand the time series approach |
| description |
© 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 |
Xue Gong Songsak Sriboonchitta Siwarat Kuson |
| author_facet |
Xue Gong Songsak Sriboonchitta Siwarat Kuson |
| author_sort |
Xue Gong |
| 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 |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005950760&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/56334 |
| _version_ |
1681424672603766784 |