The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application
As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands fo...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6663773 |
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| _version_ | 1832547662868512768 |
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| author | Tongfei Lao Xiaoting Chen Jianian Zhu |
| author_facet | Tongfei Lao Xiaoting Chen Jianian Zhu |
| author_sort | Tongfei Lao |
| collection | DOAJ |
| description | As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models. |
| format | Article |
| id | doaj-art-b26f3c6ee59a40f4803d7be18ca49cf5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b26f3c6ee59a40f4803d7be18ca49cf52025-02-03T06:43:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66637736663773The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its ApplicationTongfei Lao0Xiaoting Chen1Jianian Zhu2School of Science, Northeastern University, Shenyang 110819, ChinaSchool of Public Administration, Xiangtan University, Xiantan 411100, ChinaSchool of Public Administration, Xiangtan University, Xiantan 411100, ChinaAs a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.http://dx.doi.org/10.1155/2021/6663773 |
| spellingShingle | Tongfei Lao Xiaoting Chen Jianian Zhu The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application Complexity |
| title | The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application |
| title_full | The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application |
| title_fullStr | The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application |
| title_full_unstemmed | The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application |
| title_short | The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application |
| title_sort | optimized multivariate grey prediction model based on dynamic background value and its application |
| url | http://dx.doi.org/10.1155/2021/6663773 |
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