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Academic Journal of Computing & Information Science, 2022, 5(12); doi: 10.25236/AJCIS.2022.051211.

A study of China's population forecast based on a combination model

Author(s)

Long Wang

Corresponding Author:
Long Wang
Affiliation(s)

School of Software, Jiangxi Normal University, Nanchang, 330022, China

Abstract

The population issue has always been one of the key factors affecting the development of China. Building a strong socialist country faces a serious situation with a large population. With the rapid development of China's economy and the implementation of the national population policy, the question of how many people there will be in China in the future becomes extremely important, and the establishment of a population model, which can forecast the number of people more accurately, is a prerequisite for controlling the population growth. Therefore, this paper roots out a model to predict the future population change in China by analyzing the data related to China's population.To address problem, this paper first establishes a logistic model, and a gray prediction model. Under the assumptions, the prediction accuracy of the models is analyzed according to the historical data of China, and the results show that the accuracy of the models is high. Then, the models are used to predict the population in 2030, and the corresponding results are obtained. In order to further improve the prediction accuracy, this paper adopts the combination prediction method with unequal weights to establish the combination prediction model, calculates the weights of the prediction models separately and obtains the prediction values of the combination prediction model, and the results show that the total population of China is expected to be 150,893,000 in 2030.

Keywords

Logistic model, gray prediction model, prediction

Cite This Paper

Long Wang. A study of China's population forecast based on a combination model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 76-81. https://doi.org/10.25236/AJCIS.2022.051211.

References

[1] Tao X.X., Yang G.Y., Fang Y. Y. Prediction of total population in China based on combined prediction model [J]. Journal of Chongqing Institute of Science and Technology (Social Science Edition), 2013(07):99-103.

[2] Tian Zichen, Ji Gang, Liu Miao. Analysis and prediction of total population in Xinjiang based on improved gray GM (1, 1) model [J]. Mathematical Practice and Understanding, 2021, 51(05):258-264.

[3] Yang Zhenzhen, Liu Lin, Xie Yanqiu, Wang Miao. Research on the prediction of population aging development trend and coping strategy based on gray prediction model [J]. China Management Information Technology, 2021, 24(09):198-200.

[4] Chen Xia, Xiao Lan. Improvement of Logistic Model and Population Forecasting in China [J]. Journal of Chengdu University of Information Engineering, 2020, 35(02):239-243.

[5] Ma Xiaoxing. Census income prediction based on BP neural network [J]. Modern Computer, 2021(04):38-41.

[6] Xiao JC, Hong Han. Spatial distribution of population in urban clusters and urbanization evolution and development trend prediction [J]. Economic Journal, 2021(01):19-30+2.

[7] Zhang Shaoping. Prediction of population aging in China based on combined prediction model [J]. Party School of the CPC Qingdao Municipal Committee. Journal of Qingdao Administrative College, 2020(06):26-30+35.

[8] Zou DW, Ji JX, Zhang Y, Nie K. Correlation analysis of population influencing factors in China based on regression analysis and gray prediction [J]. Journal of Changchun Normal University, 2020, 39(06):10-17.