基于ARIMA-LSTM的货运量组合预测方法研究

Research on combined forecasting method of freight volume based on ARIMA-LSTM

针对仅考虑单一线性或非线性的货运量预测方法的不足,基于ARIMA和LSTM循环神经网络预测模型,提出ARIMA-LSTM 4种组合预测模型,实现综合考虑线性与非线性特征的货运量预测。研究结果表明:任意一种ARIMA-LSTM的组合模型的货运量预测精度均优于单一模型的,由误差倒数确定权重系数的并联组合模型预测精度最优,该模型相比于ARIMA,均方根误差降低40.66%,平均绝对误差降低29.76%,平均绝对百分比误差降低51.45%;相比于LSTM,均方根误差降低13.67%,平均绝对误差降低10.75%,平均绝对百分比误差降低36.32%,该研究可为货运量预测提供有效依据。

In view of the shortage of the existing freight volume forecasting method that can only consider the single linear or nonlinear characteristic.Four combined forecasting models were proposed based on ARIMA-LSTM.the freight volume was forecasted considering the linear and nonlinear characteristic.The results show that prediction accuracy of freight volume resulting from any kind of ARIMA-LSTM combined forecasting model is better that from single model.And error reciprocal to determine the weight coefficient of parallel combination model is optimal.Compared with the ARIMA,the root mean square error,mean absolute error,the mean absolute percentage error reduces 40.66%,29.76%and 51.45%,respectively.Compared with the LSTM,the value is the 13.67%,10.75%and36.32%,respectively.This study can provide an effective basis for freight volume forecast.