SARIMA-LSTM组合模型在铁路疫情短时客流的预测研究

Prediction research of SARIMA-LSTM combination model in the short-term railway passenger flow during epidemics

针对新型冠状病毒肺炎疫情这类突发性公共卫生事件对铁路短时客流造成的巨大扰动问题,分析疫情下的春运周期性、季节性的非平稳时间序列日客流曲线,构建基于SARIMA-LSTM的组合模型。利用SARIMA模型进行线性部分预测,LSTM滚动优化模型进行非线性部分预测,将2个预测结果代入注意机制模块加权求和,引入GRU门控循环单元辅助验证。通过对实例研究分析,结果表明:SARIMA-LSTM组合模型的预测结果控制性好,准确率高,可为疫情突发事件短时客流数据集的预测提供理论依据。

Aiming at the huge disturbance caused by sudden public health events such as the COVID-19 to the short-term railway passenger flow,this paper constructs a combination model based on SARIMA-LSTM to analyze the daily passenger flow curve of the periodic and seasonal non-stationary time series during Spring Festival transportation under the epidemic situation.The SARIMA model is used to predict the linear part,and the LSTM rolling optimization model is used for nonlinear prediction.Finally,the two prediction results are put into the weighted sum of the attention mechanism module,and the GRU gated loop unit is introduced to assist the verification.The analysis shows that the prediction results of SARIMA-LSTM combination model have good control and high accuracy,which can provide theoretical basis for the prediction of the short-term passenger flow data set of epidemic emergencies.