Welcome to Francis Academic Press

The Frontiers of Society, Science and Technology, 2021, 3(6); doi: 10.25236/FSST.2021.030611.

Number regression and prediction of Chinese seniors based on the ARIMA model

Author(s)

Yingshuo Li1, Yu Liu2

Corresponding Author:
Yingshuo Li
Affiliation(s)

1Qingdao University, Qingdao, Shandong, 266000, China

2Weihai Campus of Shandong University, Weihai, Shandong, 264200, China

Abstract

China's aging population developed rapidly in the 21st century, It has brought a significant impact on the changes in China's social security system and labor force structure, In 2021, the "seven general" data show that the aging process of China's society continues to climb, This paper will be based on the number of 65 in China from 1990-2020, Using the time-series ARIMA model, , Differential ideas derived from data stationarity, Unit root inspection, Model order, AIC and BIC coefficients in contrast, Model error analysis, As well as model prediction analysis, the number of elderly population in China from 2021 to 2030, The research results show that the number of elderly people in China is still increasing in the next few years, And put forward the corresponding policy suggestions for the results.

Keywords

Time series, ARIMA model, Population aging, Population prediction

Cite This Paper

Yingshuo Li, Yu Liu. Number regression and prediction of Chinese seniors based on the ARIMA model. The Frontiers of Society, Science and Technology (2021) Vol. 3, Issue 6: 68-71. https://doi.org/10.25236/FSST.2021.030611.

References

[1] Lu Jiehua, Liu Qin.Characteristics, influence and coping strategies of new forms of Chinese aging society —— is based on the interpretation of "seven pu" data [J]. Population and Economy, 2021 (05): 13-24.

[2] Original New, Taurus.Outlook and impact response of negative world population growth [J].Proceedings of Hebei University (Philosophy and Social Sciences Edition), 2021, 46 (01): 82-91.

[3] Sun Ting, Wang Xueqin.—— Policy Text Analysis Based on 1949-2020 [J].Chongqing Social Sciences, 2021 (09): 38-52...

[4] Cao Yanan, Cui Yujie.Study on the prediction of Chinese population based on Logistic population model [J]. Modern marketing (Scholastic Edition), 2021 (08): 192-193.

[5] Yan Yujun, Yan Yun Lou.Aging trend and influencing factors —— based on GM(1,1) and principal component analysis [J].Chinese Journal of Gerontology, 2021,41 (14): 3093-3098.