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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sompop Moonchai | en_US |
dc.contributor.author | Wanwisa Rakpuang | en_US |
dc.date.accessioned | 2018-09-04T10:12:58Z | - |
dc.date.available | 2018-09-04T10:12:58Z | - |
dc.date.issued | 2015-01-01 | en_US |
dc.identifier.issn | 16875605 | en_US |
dc.identifier.issn | 16875591 | en_US |
dc.identifier.other | 2-s2.0-84953214867 | en_US |
dc.identifier.other | 10.1155/2015/126738 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward | en_US |
dc.identifier.uri | http://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.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.subject | Mathematics | en_US |
dc.title | A new approach to improve accuracy of grey model GMC (1,n) in time series prediction | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Modelling and Simulation in Engineering | en_US |
article.volume | 2015 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
article.stream.affiliations | Rajabhat University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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