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Influencing factors analysis and development trend prediction of population aging in Wuhan based on TTCCA and MLRA-ARIMA

Authors
  • Rao, Congjun1
  • Gao, Yun1
  • 1 Wuhan University of Technology,
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Jan 15, 2021
Pages
1–25
Identifiers
DOI: 10.1007/s00500-020-05553-9
PMID: 33469406
PMCID: PMC7809647
Source
PubMed Central
Keywords
License
Unknown

Abstract

With the rapid development of the economy, the problem of population aging has become increasingly prominent. To analyse the key factors affecting population aging effectively and predict the development trend of population aging timely are of great significance for formulating relevant policies scientifically and reasonably, which can mitigate the effects of population aging on society. This paper analyses the current situation of population aging in Wuhan of China and discusses the main factors affecting the population aging quantitatively, and then establishes a combination prediction model to forecast the population aging trend. Firstly, considering the attribute values of the primary influence factors are multi-source heterogeneous data (the real numbers, interval numbers and fuzzy linguistic variables coexist), a two-tuple correlation coefficient analysis method is proposed to rank the importance of the influencing factors and to select the main influencing factors. Secondly, a combination prediction model named Multiple Linear Regression Analysis-Autoregressive Integrated Moving Average is established to predict the number and the proportion of aging population in Wuhan. By using the statistical data of Wuhan in the past 20 years, this combination prediction model is used for empirical analysis, and a prediction result of the number and the proportion of aging people in Wuhan in the future is obtained. Based on these quantitative analysis results, we propose some countermeasures and suggestions on how to alleviate the population aging of Wuhan from aspects of economic development, pension security system design and policy formulation, which provide theoretical basis and method reference for relevant population management departments to make scientific decisions.

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