基于GM-LSTM模型的南京市老年人口预测研究

Research on Elderly Population Prediction Based on GM-LSTM Model in Nanjing City

当前,中国人口老龄化问题日益突出,准确预测未来老年人口数量是夯实形势政策研究的基础性工作,对于相关政策的制定和社会发展具有重要的参考价值。文中提出了GM-LSTM模型,该模型将灰色系统动态模型与LSTM深度学习神经网络的优势相结合,构建组合模型,利用LSTM神经网络模型修正GM预测模型中估计序列与原序列的残差。模型验证表明,GM-LSTM模型具备良好的预测精度和泛化能力。利用GM-LSTM模型,选取2008-2017年的数据进行分析,预测2021-2035年南京市各行政区老年人口数量及密度。结果表明,南京市各行政区未来15年老年人口数量呈现出高基数、高增长的态势,且各行政区之间老年人口密度差异显著,中心城区的老年人口密度较高,成为老年密集区,人口密度随着近郊区方向逐渐递减。

At present,the aging of China’s population is becoming increasingly prominent.Accurate prediction of the number of the elderly population in the future is the basic work to consolidate the situation and policy research,which has important reference value for the formulation of relevant policies and social development.In this paper,a GM-LSTM model is proposed,which combines the gray system dynamic model with the advantages of LSTM deep learning neural network to build a composite model,and the LSTM neural network model is used to modify the residual of the estimated sequence and the original sequence in the GM prediction model.The model verification shows that the GM-LSTM model has good prediction accuracy and generalization ability.Using GM-LSTM model for analysis,data from 2008 to 2017 are selected for analysis to predict the number and density of the elderly population in all administrative areas of Nanjing from 2021 to 2035.The results show that in the next 15 years,the number of elderly population in each administrative area of Nanjing shows a trend of high base and high growth,and the elderly population density difference among administrative areas is significant.The elderly population density in the central urban area is relatively high,which makes it a densely populated area,and the population density gradually decreases along with the direction of the suburbs.