A Review of Population Structure Dynamics and Risk Warning Based on Time Series Models

Abstract

 In the fields of population data analysis and sociological research, time series models, particularly ARIMA and VAR models, are widely applied and highly regarded for their stability and interpretability. This paper systematically reviews the application of ARIMA models in modeling and forecasting key demographic indicators, such as total population size and aging rates. This model excels at capturing temporal patterns and trends within univariate sequences, making it a core tool for population forecasting. Furthermore, this paper employs VAR models to explore the multifaceted societal impacts of population aging. VAR models excel at analyzing the interactive dynamics among multiple variables such as aging, economic growth, and social welfare expenditures. By integrating the risk assessment capabilities of VAR models, this study aims to summarize the challenges posed by demographic shifts and the practical applications of ARIMA and VAR models in population data, providing foundational theoretical support for evidence-based policy design.