A New Approach to Improve Accuracy of Grey Model GMC(1,n) in Time Series Prediction
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 ba...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/126738 |
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| Summary: | 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 CO2 emission 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. |
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| ISSN: | 1687-5591 1687-5605 |