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Title: Comparing linear and nonlinear models in forecasting telephone subscriptions using likelihood based belief functions
Authors: Noppasit Chakpitak
Woraphon Yamaka
Songsak Sriboonchitta
Keywords: Computer Science
Issue Date: 1-Jan-2018
Abstract: © 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%.
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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