Research on the prediction of outpatient volume based on SARIMA-LSTM

Abstract

In order to achieve more robust and accurate outpatient volume prediction,a hybrid prediction model based on SARIMALSTM was constructed.SARIMA model was used to build a single index model of outpatient volume to extract the cycle,trend and other information contained in outpatient volume index.Then multiple related indexes,including holiday days,legal working days,average maximum temperature,were used as input of a many-to-one LSTM model,in order to further learn the residual of SARIMA model and extract the nonlinear relationship between residual and multiple variables.The empirical results show that the SARIMA-LSTM hybrid model constructed in this paper has higher prediction accuracy than the five mainstream prediction methods,so it has good practical application value.

Keywords

• seasonal auto-regressive integrated moving average model;long short term memory;outpatient forecast;residual