Prediction of China’s population age structure based on multi-parameter gray Bernoulli model

Population age structure changes can significantly impact economic dynamics, exerting profound effects on societal sustainable development. The establishment of an efficient population age structure prediction system relies on precise and consistent forecasting. In this context, this paper introduces a robust multi-parameter gray Bernoulli prediction model as a foundation for such a system. Through comprehensive performance tests, we demonstrate that the proposed model yields notably accurate predictions. Furthermore, we conduct comparative experiments on data preprocessing, revealing that the processed data significantly reduces prediction errors. The model is further enhanced using the Particle Swarm Optimization (PSO) algorithm to optimize its performance. Subsequently, we conduct point predictions and interval predictions for various age groups, facilitating a nuanced analysis of age structure changes. The findings contribute to informed decision-making recommendations based on the observed trends. This research underscores the importance of accurate population age structure prediction for informed socio-economic planning and development.