A new approach to improve accuracy of grey model GMC (1,n) in time series prediction

© 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by...

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Main Authors:	Sompop Moonchai, Wanwisa Rakpuang
Format:	Journal
Published:	2018
Subjects:	Computer Science Engineering Mathematics
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54404
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-54404
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spelling	th-cmuir.6653943832-544042018-09-04T10:19:50Z A new approach to improve accuracy of grey model GMC (1,n) in time series prediction Sompop Moonchai Wanwisa Rakpuang Computer Science Engineering Mathematics © 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n) model was verified by two cases: the study of forecasting CO2emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n) model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n) and D-GMC(1,n) models. 2018-09-04T10:12:58Z 2018-09-04T10:12:58Z 2015-01-01 Journal 16875605 16875591 2-s2.0-84953214867 10.1155/2015/126738 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54404
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science Engineering Mathematics
spellingShingle	Computer Science Engineering Mathematics Sompop Moonchai Wanwisa Rakpuang A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
description	© 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n) model was verified by two cases: the study of forecasting CO2emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n) model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n) and D-GMC(1,n) models.
format	Journal
author	Sompop Moonchai Wanwisa Rakpuang
author_facet	Sompop Moonchai Wanwisa Rakpuang
author_sort	Sompop Moonchai
title	A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
title_short	A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
title_full	A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
title_fullStr	A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
title_full_unstemmed	A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
title_sort	new approach to improve accuracy of grey model gmc (1,n) in time series prediction
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54404
_version_	1681424314357776384

A new approach to improve accuracy of grey model GMC (1,n) in time series prediction

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