Education, Science, Technology, Innovation and Life
Open Access
Sign In

Population Prediction of China Based on ARIMA-LSTM Combined Model

Download as PDF

DOI: 10.23977/jsoce.2023.050304 | Downloads: 20 | Views: 481

Author(s)

Gu Minghui 1

Affiliation(s)

1 College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China

Corresponding Author

Gu Minghui

ABSTRACT

In recent years, the birth rate in China has been declining continuously. Accurate population prediction is of great significance for the government to formulate population macro-adjustment policies. In this paper, the residual optimization method is used to establish the combined prediction model of ARIMA-LSTM. It is found that the precision of combinatorial prediction is better than that of each single prediction, and the combinatorial prediction with residual optimization has better generalization ability. In addition, the forecast results show that China's population will continue to decline in 2023.

KEYWORDS

ARIMA; LSTM; Population gross

CITE THIS PAPER

Gu Minghui, Population Prediction of China Based on ARIMA-LSTM Combined Model. Journal of Sociology and Ethnology (2023) Vol. 5: 19-25. DOI: http://dx.doi.org/10.23977/jsoce.2023.050304.

REFERENCES

[1] Zhang T L. Application of GM (1, 1) model in predicting population birth rate [J]. Chinese Journal of Health Statistics, 2000(02):26-27.
[2] Yin C H, Chen L. Research and application of population prediction model based on BP neural network [J]. Population Journal, 2005(02):44-48.
[3] Rayer S, Smith S K, Tayman J. Empirical Prediction Intervals for County Population Forecasts [J]. Springer Open Choice, 2009, 28(6): 773-793.
[4] Mao J H. Combination prediction of birth rate in China based on IOWA operator [J]. Journal of Changchun Institute of Technology (Natural Science Edition), 2018, 19(01):120-124.
[5] Chen L, Mu T, Li X, et al. Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models [J]. Sustainability, 2022, 14.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.