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The analysis and forecast of Chinese population using ARIMA model
- 1 Henan Experimental High School
* Author to whom correspondence should be addressed.
Abstract
Although China's population problem has always attracted much attention, most of the literature focuses on China's family planning policy and China's aging population, and there is a lack of analysis and research on China's population forecast. However, China's population problem is closely related to the economic development of China's future society, the contradiction between people and land, the trend of population aging and environmental pollution, therefore, this paper decides to use the time series model in R language to predict the future population of China. The method used throughout the process was time series and linear regression in R language, and the data of Chinese population in the past years was fit in the ARIMA models to make forecast for Chinese future population. The results show that in the next decade, China's population growth rate is still showing a trend of further decline, and in the next period of time, this trend is difficult to change. China's total population is generally growing slowly, and the status quo of a large and slow population base is unlikely to change in the near future.
Keywords
Chinese population, demography, prediction, time series
[1]. Liu C 1989 Characteristics of the population studies in China. Chin J Popul Sci, 1(4), 375-379.
[2]. hennan S and Sear R 2021 Archaeology, demography and life history theory together can help us explain past and present population patterns. Philos Trans R Soc Lond B Biol Sci, 18, 376.
[3]. Bao J, Tang Q and Chen Y 2018 Individual nursing care for the elderly among China's aging population. Biosci Trends, 11(6), 694-696.
[4]. Okui T 2023 Association between infant mortality and parental educational level: An analysis of data from Vital Statistics and Census in Japan. PLoS One, 18(6), 286530.
[5]. Smallwood S and Lynch K 2010 An analysis of patient register data in the Longitudinal Study - what does it tell us about the quality of the data? Popul Trends, 141, 148-66.
[6]. Wang D, Wang S J, Xu J and Lababidi S 2022 A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine. Pharm Stat, 21(3), 584-598.
[7]. Coleman M P 2020 Axon Degeneration: Which Method to Choose? Methods Mol Biol, 21(43), 3-12.
[8]. Sevilimedu V and Yu L 2022 Simulation extrapolation method for measurement error: A review. Stat Methods Med Res, 31(8), 1617-1636.
[9]. Jin W, McCue S W and Simpson M J 2018 Extended logistic growth model for heterogeneous populations. J Theor Biol, 14, 51-61.
[10]. Reeves S and Bernstein I M 2008 Optimal growth modeling. Semin Perinatol, 32(3),148-153.
[11]. Peleg M and Corradini M G 2011 Microbial growth curves: what the models tell us and what they cannot. Crit Rev Food Sci Nutr, 51(10), 917-945.
Cite this article
Yang,S. (2023). The analysis and forecast of Chinese population using ARIMA model. Theoretical and Natural Science,26,273-278.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
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