Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55978
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dc.contributor.authorNantiworn Thianpaenen_US
dc.contributor.authorJianxu Liuen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T03:06:59Z-
dc.date.available2018-09-05T03:06:59Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn16860209en_US
dc.identifier.other2-s2.0-85008185815en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008185815&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55978-
dc.description.abstract© 2016 by the Mathematical Association of Thailand. All rights reserved. This paper aims at applying a recent new approach to predicting the growth rate of Thailand GDP. The new approach will provide uncertainty about predicted values solely from observed data without the need to supply some subjective prior distribution on unknown model parameters. This is achieved by building a belief function (i.e., a distribution of a random set) from the likelihood function given the observed data, and use it to assess prediction uncertainty. With our sampling model as an autoregressive time series model, we demonstrate em-pirically that this approach can provide a reliable con_dence interval for predicted values.en_US
dc.subjectMathematicsen_US
dc.titleTime series forecast using AR-belief approachen_US
dc.typeJournalen_US
article.title.sourcetitleThai Journal of Mathematicsen_US
article.volume14en_US
article.stream.affiliationsRajabhat Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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