Forecasting inbound tourism demand to China using time series models and belief functions

© Springer International Publishing Switzerland 2015. Modeling uncertainty is a key issue in forecasting. In the tourism area, forecasts are used by governments, airline companies and operators to design tourism policies and they should include a quantification of uncertainties. This paper proposed...

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Main Authors:	Jiechen Tang, Songsak Sriboonchitta, Xinyu Yuan
Format:	Book Series
Published:	2018
Subjects:	Agricultural and Biological Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84919360820&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44653
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-44653
record_format	dspace
spelling	th-cmuir.6653943832-446532018-04-25T07:53:39Z Forecasting inbound tourism demand to China using time series models and belief functions Jiechen Tang Songsak Sriboonchitta Xinyu Yuan Agricultural and Biological Sciences © Springer International Publishing Switzerland 2015. Modeling uncertainty is a key issue in forecasting. In the tourism area, forecasts are used by governments, airline companies and operators to design tourism policies and they should include a quantification of uncertainties. This paper proposed a new approach to forecast the tourism demand, which is time series models combined with belief functions. We used this method to predict the demand for China international tourism, with an explicit representation of forecast uncertainty. The monthly data of international tourist arrival cover the period from January 1991 to June 2013. The result show that time seriesmodels combined with belief functions is a computationally simple and effective method. 2018-01-24T04:46:09Z 2018-01-24T04:46:09Z 2015-01-01 Book Series 1860949X 2-s2.0-84919360820 10.1007/978-3-319-13449-9_23 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84919360820&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44653
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Agricultural and Biological Sciences
spellingShingle	Agricultural and Biological Sciences Jiechen Tang Songsak Sriboonchitta Xinyu Yuan Forecasting inbound tourism demand to China using time series models and belief functions
description	© Springer International Publishing Switzerland 2015. Modeling uncertainty is a key issue in forecasting. In the tourism area, forecasts are used by governments, airline companies and operators to design tourism policies and they should include a quantification of uncertainties. This paper proposed a new approach to forecast the tourism demand, which is time series models combined with belief functions. We used this method to predict the demand for China international tourism, with an explicit representation of forecast uncertainty. The monthly data of international tourist arrival cover the period from January 1991 to June 2013. The result show that time seriesmodels combined with belief functions is a computationally simple and effective method.
format	Book Series
author	Jiechen Tang Songsak Sriboonchitta Xinyu Yuan
author_facet	Jiechen Tang Songsak Sriboonchitta Xinyu Yuan
author_sort	Jiechen Tang
title	Forecasting inbound tourism demand to China using time series models and belief functions
title_short	Forecasting inbound tourism demand to China using time series models and belief functions
title_full	Forecasting inbound tourism demand to China using time series models and belief functions
title_fullStr	Forecasting inbound tourism demand to China using time series models and belief functions
title_full_unstemmed	Forecasting inbound tourism demand to China using time series models and belief functions
title_sort	forecasting inbound tourism demand to china using time series models and belief functions
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84919360820&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44653
_version_	1681422599221936128

Forecasting inbound tourism demand to China using time series models and belief functions

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