Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions

© Springer International Publishing AG 2018. In this paper, we experiment with several different models with belief function to forecast Thai telephone subscribers. This approach will provide an uncertainty about predicted values and yield a predictive belief function that quantities the uncertainty...

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Main Authors:	Noppasit Chakpitak, Woraphon Yamaka, Songsak Sriboonchitta
Format:	Book Series
Published:	2018
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037842432&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43874
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Institution:	Chiang Mai University

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spelling	th-cmuir.6653943832-438742018-01-24T04:14:35Z Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions Noppasit Chakpitak Woraphon Yamaka Songsak Sriboonchitta © Springer International Publishing AG 2018. In this paper, we experiment with several different models with belief function to forecast Thai telephone subscribers. This approach will provide an uncertainty about predicted values and yield a predictive belief function that quantities the uncertainty about the future data. The proposed forecasting models include linear AR, Kink AR, Threshold AR, and Markov Switching AR models. Next, we compare the out-of-sample performance using RMSE and MAE. The results suggest that the out-of-sample belief function based KAR forecast is more accurate than other models. Finally, we find that the growth rate of Thai telephone subscription in 2016 will fall around 6.08%. 2018-01-24T04:14:35Z 2018-01-24T04:14:35Z 2018-01-01 Book Series 1860949X 2-s2.0-85037842432 10.1007/978-3-319-70942-0_26 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037842432&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43874
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
description	© Springer International Publishing AG 2018. In this paper, we experiment with several different models with belief function to forecast Thai telephone subscribers. This approach will provide an uncertainty about predicted values and yield a predictive belief function that quantities the uncertainty about the future data. The proposed forecasting models include linear AR, Kink AR, Threshold AR, and Markov Switching AR models. Next, we compare the out-of-sample performance using RMSE and MAE. The results suggest that the out-of-sample belief function based KAR forecast is more accurate than other models. Finally, we find that the growth rate of Thai telephone subscription in 2016 will fall around 6.08%.
format	Book Series
author	Noppasit Chakpitak Woraphon Yamaka Songsak Sriboonchitta
spellingShingle	Noppasit Chakpitak Woraphon Yamaka Songsak Sriboonchitta Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
author_facet	Noppasit Chakpitak Woraphon Yamaka Songsak Sriboonchitta
author_sort	Noppasit Chakpitak
title	Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
title_short	Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
title_full	Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
title_fullStr	Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
title_full_unstemmed	Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
title_sort	comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037842432&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43874
_version_	1681422454653714432

Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions

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