Analysis and Prediction of Early Retirement Status Based on SARIMA and LSTM——Taking Tianjin as an Example
With the ageing of China's population,the delayed retirement policy has become a major trend.However,in reality,the phenomenon of early retirement is still serious,which has a negative impact on economic and social development.This paper took Tianjin as an example,focusing on the special phenomenon of early retirement due to illness or non-work-related disability,explored the influencing factors of early retirement by using data visualization and decision tree algorithm,and established the SARIMA prediction model and LSTM model to predict the total number of employees who will retire early and the number of employees who will retire early due to a certain type of illness,respectively.The study found that gender,age and type of organisation have an impact on the illnesses of early-retirement workers;overall,the number of future early-retirement workers due to illnesses in Tianjin shows an increasing trend in a short period of time,and then decreases.The results of the study are intended to make targeted recommendations to provide data support and theoretical basis for improving the retirement system in China.
early retirementSARIMA time seriesLSTM neural network
NETL
NSTL
万方数据
