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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| Main Authors: | , , , |
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| Format: | Journal |
| Published: |
2017
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| Online Access: | 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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| Institution: | Chiang Mai University |
| Summary: | © 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. |
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