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:	Xue Gong, Songsak, Sriboonchitta, Siwarat Kuson
Format:	Journal
Published:	2018
Subjects:	Social Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005950760&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/56334
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-56334
record_format	dspace
spelling	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

Forecasting the Chinese tourist arrivals to Thailand the time series approach

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