A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications
Accurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain i...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4564653 |
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| _version_ | 1832567492102324224 |
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| author | Song Ding Ruojin Li |
| author_facet | Song Ding Ruojin Li |
| author_sort | Song Ding |
| collection | DOAJ |
| description | Accurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain issues, instead of the incorrect multivariable grey model (GM (1, n)). However, the conventional approach to computing background values of the GMC (1, n) model is inaccurate, and this model’s forecasting accuracy cannot be expected. Thereby, the drawback analysis of the GMC (1, n) model is conducted with mathematical reasoning, which can explain why this model is inaccurate in some applications. In order to eliminate the drawbacks, a new optimized GMC (1, n), shorted for OGMC (1, n), is proposed, whose background values are calculated based on Simpson’ rule that is able to efficiently approximate the integration of a function. Furthermore, its extended version that uses the Gaussian rule to discretize the convolution integral, abbreviated as OGMCG (1, n), is proposed to further enhance the model’s forecasting ability. In general, these two optimized models have such advantages as simplified structure, consistent forecasting performance, and satisfactory efficiency. Three empirical studies are carried out for verifying the above advantages of the optimized model, compared with the conventional GMC (1, n), GMCG (1, n), GM (1, n), and DGM (1, n) models. Results show that the new background values can effectively be calculated based on Simpson’s rule, and the optimized models significantly outperform other competing models in most cases. |
| format | Article |
| id | doaj-art-0c9a1d843da74974b13c98032918c005 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0c9a1d843da74974b13c98032918c0052025-02-03T01:01:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/45646534564653A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its ApplicationsSong Ding0Ruojin Li1School of Economics, Zhejiang University of Finance & Economics, Hangzhou 211106, ChinaSchool of Economics, Zhejiang University of Finance & Economics, Hangzhou 211106, ChinaAccurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain issues, instead of the incorrect multivariable grey model (GM (1, n)). However, the conventional approach to computing background values of the GMC (1, n) model is inaccurate, and this model’s forecasting accuracy cannot be expected. Thereby, the drawback analysis of the GMC (1, n) model is conducted with mathematical reasoning, which can explain why this model is inaccurate in some applications. In order to eliminate the drawbacks, a new optimized GMC (1, n), shorted for OGMC (1, n), is proposed, whose background values are calculated based on Simpson’ rule that is able to efficiently approximate the integration of a function. Furthermore, its extended version that uses the Gaussian rule to discretize the convolution integral, abbreviated as OGMCG (1, n), is proposed to further enhance the model’s forecasting ability. In general, these two optimized models have such advantages as simplified structure, consistent forecasting performance, and satisfactory efficiency. Three empirical studies are carried out for verifying the above advantages of the optimized model, compared with the conventional GMC (1, n), GMCG (1, n), GM (1, n), and DGM (1, n) models. Results show that the new background values can effectively be calculated based on Simpson’s rule, and the optimized models significantly outperform other competing models in most cases.http://dx.doi.org/10.1155/2020/4564653 |
| spellingShingle | Song Ding Ruojin Li A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications Complexity |
| title | A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications |
| title_full | A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications |
| title_fullStr | A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications |
| title_full_unstemmed | A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications |
| title_short | A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications |
| title_sort | new multivariable grey convolution model based on simpson s rule and its applications |
| url | http://dx.doi.org/10.1155/2020/4564653 |
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