%0 Journal Article
%T 基于新陈代谢GM-ARIMA组合模型的广东省人口老龄化预测研究<br>Prediction of Population Ageing in Guangdong Province Based on Metabolic GM-ARIMA Combined Model
%A 张集锦
%J Statistics and Applications
%P 385-396
%@ 2325-226X
%D 2024
%I Hans Publishing
%R 10.12677/sa.2024.132038
%X 本文基于2000~2022年广东省人口结构数据，利用组合权重系数法构建新陈代谢GM(1, 1)-ARIMA模型对其未来人口结构变化情况进行预测研究。为了弥补ARIMA模型样本需求量高，拟合更多反映线性趋势的劣势，克服传统灰色预测在中长期预测的不可操作性和指数爆炸增长导致的预测偏离问题，首先利用最小二乘、MAPE和组合权重系数法构建基于新陈代谢GM(1, 1)-ARIMA的组合预测模型，接着引入TIC，MAPE和RMSE三个评价指标评估不同组合模型的精度，最终选取利用组合权重系数法构建的组合模型进行拟合预测。预测结果表明：该组合模型比单项模型预测精度提高0.32%，具有参考价值。同时表明广东人口年龄结构较为年轻，但进入初始少子化社会，未来将不可避免地以大规模、高速度进入深度老龄化和超老龄化社会，需引起高度重视。<br />
Based on the demographic data of Guangdong Province from 2000 to 2022, the article uses the combined weight coefficient method to construct a metabolic GM(1, 1)-ARIMA model to forecast its future demographic changes. In order to make up for the disadvantages of the ARIMA model with high sample requirement and fitting more reflective of a linear trend, and to overcome the inoperability of traditional grey prediction in medium and long-term prediction and the problem of prediction deviation caused by exponential explosive growth, firstly, the combined prediction model based on metabolism GM(1, 1)-ARIMA was constructed by using the method of least squares, MAPE and combined weight coefficients, and then, the three models of TIC, MAPE and RMSE were introduced to assess the accuracy of different combinatorial models, and finally the combinatorial model constructed using the combined weight coefficient method was selected for fitting prediction. The prediction results show that the combined model is 0.32% more accurate than the single model, which is of reference value. It also shows that the age structure of Guangdong’s population is relatively young, but it has entered the initial oligocephalic society, and will inevitably enter the deep aging and super-aging society in the future on a large scale and at a high speed, which needs to be paid great attention to.
%K 人口老龄化，新陈代谢GM(1
%K 1)，ARIMA模型，组合预测模型 <br>Population Ageing
%K Metabolism GM(1
%K 1)
%K ARIMA Model
%K Combined Forecasting Model
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=84800