Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54404
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dc.contributor.authorSompop Moonchaien_US
dc.contributor.authorWanwisa Rakpuangen_US
dc.date.accessioned2018-09-04T10:12:58Z-
dc.date.available2018-09-04T10:12:58Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn16875605en_US
dc.identifier.issn16875591en_US
dc.identifier.other2-s2.0-84953214867en_US
dc.identifier.other10.1155/2015/126738en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54404-
dc.description.abstract© 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.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.titleA new approach to improve accuracy of grey model GMC (1,n) in time series predictionen_US
dc.typeJournalen_US
article.title.sourcetitleModelling and Simulation in Engineeringen_US
article.volume2015en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsRajabhat Universityen_US
Appears in Collections:CMUL: Journal Articles

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