Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education.
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| Title: | Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education. |
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| Authors: | Chao Ge1 author@boulder.nist.gov, Jiaqi Xie1 a82375051@qq.com |
| Source: | International Journal of Emerging Technologies in Learning. 2015, Vol. 10 Issue 8, p30-33. 4p. 2 Diagrams, 1 Chart, 3 Graphs. |
| Subject Terms: | *Education costs, *Decision making, *Universities & colleges, Gray forecasting model, Parameter estimation, Artificial neural networks |
| Abstract: | The forecast of the cost of education in university is conducive to strengthening the management of the cost of education, mining the potential of reducing the cost, improving the management level and improving the use efficiency of the funds. Through accounting and forecasting of the cost of education in university, we can make the school to plan the cost and quota index according to its own practical needs, so as to improve the financial system and cost management system of university education. This will enable the university to carry out the correct decision-making, and provide support for the preparation of financial budget and long-term planning. At present, there are some defects in existing method of the university education cost prediction. The unitary regression method is very difficult to effectively remove the noise value in the fitting. Artificial neural network model is applied to predict the big data. Although the traditional gray forecasting model has a good prediction effect with the less data and poor information, the model still has the disadvantage that the background value is not smooth enough. In order to solve the above problems, this paper proposes an adaptive residual correction method based on grey system theory, and improves the grey forecasting model. This method can effectively remove the noise in the original data sequence, and it can be used to predict the cost of university education in China. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 111650611 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chao+Ge%22">Chao Ge</searchLink><relatesTo>1</relatesTo><i> author@boulder.nist.gov</i><br /><searchLink fieldCode="AR" term="%22Jiaqi+Xie%22">Jiaqi Xie</searchLink><relatesTo>1</relatesTo><i> a82375051@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2015, Vol. 10 Issue 8, p30-33. 4p. 2 Diagrams, 1 Chart, 3 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Education+costs%22">Education costs</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Universities+%26+colleges%22">Universities & colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+forecasting+model%22">Gray forecasting model</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The forecast of the cost of education in university is conducive to strengthening the management of the cost of education, mining the potential of reducing the cost, improving the management level and improving the use efficiency of the funds. Through accounting and forecasting of the cost of education in university, we can make the school to plan the cost and quota index according to its own practical needs, so as to improve the financial system and cost management system of university education. This will enable the university to carry out the correct decision-making, and provide support for the preparation of financial budget and long-term planning. At present, there are some defects in existing method of the university education cost prediction. The unitary regression method is very difficult to effectively remove the noise value in the fitting. Artificial neural network model is applied to predict the big data. Although the traditional gray forecasting model has a good prediction effect with the less data and poor information, the model still has the disadvantage that the background value is not smooth enough. In order to solve the above problems, this paper proposes an adaptive residual correction method based on grey system theory, and improves the grey forecasting model. This method can effectively remove the noise in the original data sequence, and it can be used to predict the cost of university education in China. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijet.v10i8.5215 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 30 Subjects: – SubjectFull: Education costs Type: general – SubjectFull: Decision making Type: general – SubjectFull: Universities & colleges Type: general – SubjectFull: Gray forecasting model Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chao Ge – PersonEntity: Name: NameFull: Jiaqi Xie IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 12 Text: 2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 10 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
| ResultId | 1 |
