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

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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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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 71671135, 72071150), the 2019 Fundamental Research Funds for the Central Universities (WUT: 2019IB013), and the China Scholarship Council (CSC) scholarship (Grant No. 201906955002).

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  1. School of Science, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Congjun Rao & Yun Gao

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  1. Congjun Rao
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Correspondence to Congjun Rao.

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Rao, C., Gao, Y. Influencing factors analysis and development trend prediction of population aging in Wuhan based on TTCCA and MLRA-ARIMA. Soft Comput 25, 5533–5557 (2021). https://doi.org/10.1007/s00500-020-05553-9

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