Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021)

Forecast of National Higher Education Scale Trend Based on GM (1,1) Model

Authors
Feiyan Shen, Mengxia Yang, Wei Deng, Sijia Zhong
Corresponding Author
Feiyan Shen
Available Online 8 April 2021.
DOI
https://doi.org/10.2991/assehr.k.210407.047How to use a DOI?
Keywords
GM (1,1), residual tests, MATLAB
Abstract

The three indicators of the number of schools, educational personnel and full-time teachers have a correlation with college size, which can be quantified. Therefore, by collecting data from the last five years in China, a grey forecasting model (GM) was used to predict the quantitative changes of these three indicators in the coming years and to propose targeted policies for implementation. The predicted values are analysed by residual tests for accuracy. The effectiveness of the policy is evaluated by passing the accuracy test.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
8 April 2021
ISBN
978-94-6239-360-8
ISSN
2352-5398
DOI
https://doi.org/10.2991/assehr.k.210407.047How to use a DOI?
Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Feiyan Shen
AU  - Mengxia Yang
AU  - Wei Deng
AU  - Sijia Zhong
PY  - 2021
DA  - 2021/04/08
TI  - Forecast of National Higher Education Scale Trend Based on GM (1,1) Model
BT  - Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021)
PB  - Atlantis Press
SP  - 233
EP  - 237
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210407.047
DO  - https://doi.org/10.2991/assehr.k.210407.047
ID  - Shen2021
ER  -