Time series forecast using AR-belief approach

© 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 subject...

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Main Authors:	Nantiworn Thianpaen, Jianxu Liu, Songsak Sriboonchitta
Format:	Journal
Published:	2018
Subjects:	Mathematics
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008185815&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55978
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-55978
record_format	dspace
spelling	th-cmuir.6653943832-559782018-09-05T03:06:59Z Time series forecast using AR-belief approach Nantiworn Thianpaen Jianxu Liu Songsak Sriboonchitta Mathematics © 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. 2018-09-05T03:06:59Z 2018-09-05T03:06:59Z 2016-01-01 Journal 16860209 2-s2.0-85008185815 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008185815&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55978
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Mathematics
spellingShingle	Mathematics Nantiworn Thianpaen Jianxu Liu Songsak Sriboonchitta Time series forecast using AR-belief approach
description	© 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.
format	Journal
author	Nantiworn Thianpaen Jianxu Liu Songsak Sriboonchitta
author_facet	Nantiworn Thianpaen Jianxu Liu Songsak Sriboonchitta
author_sort	Nantiworn Thianpaen
title	Time series forecast using AR-belief approach
title_short	Time series forecast using AR-belief approach
title_full	Time series forecast using AR-belief approach
title_fullStr	Time series forecast using AR-belief approach
title_full_unstemmed	Time series forecast using AR-belief approach
title_sort	time series forecast using ar-belief approach
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008185815&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55978
_version_	1681424606137679872

Time series forecast using AR-belief approach

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