计算机工程与应用2024，Vol.60Issue(19)：297-308,12.DOI:10.3778/j.issn.1002-8331.2309-0081
零担物流时序预测的SARIMA-GRU-BPNN组合模型及应用
Combined SARIMA-GRU-BPNN Model for LTL Logistics Time Series Prediction and Application
摘要
Abstract
Aiming at the problem that the significant seasonal,nonlinear,and stochastic characteristics of the demanded material flow of less-than-truck-load logistics(LTL)make it difficult to predict,a prediction method of SARIMA-GRU-BPNN combined model for LTL time series prediction is proposed.The seasonal decomposition model is used to decom-pose the logistics flow into trend,seasonality,and residual,the seasonal difference autoregressive moving average model(SARIMA)is used to fit the linear change for the trend component,the gated recurrent neural network(GRU)is used to fit the seasonal change for the seasonal component,and the back-propagation neural network(BPNN)is used to fit the nonlinear and stochastic change for the residual component,and the combination reconstruction is used to get the final pre-diction value.Based on the experimental results,the root mean square error(RMSE)is decreased by 31.5%,34.5%,and 47.1%when compared to single self-contained models SARIMA,GRU,and BPNN,respectively.Additionally,the RMSE is reduced by 71.3%,68.9%,54.4%,and 70.7%when compared to other single models gray model,support vector ma-chines,long and short-term memory networks,and multiple linear regression,respectively.Furthermore,the RMSE is re-duced by 71.3%,68.9%,and 54.4%when compared to combined models gray model,support vector machines,and long and short-term memory networks,respectively.In comparison to combined models ARIMA-GRU,ARIMA-BPNN,and ARIMA-SVM,the RMSE is reduced by 31.0%,43.0%,and 56.1%,respectively.The goodness-of-fit of the trend and sea-sonal component prediction models reaches 92%and 99%,effectively reducing the overall prediction error and improving prediction accuracy and model robustness.关键词
零担物流/需求预测/时序分解/组合模型/人工神经网络Key words
less-than-truck-load logistics(LTL)/demand prediction/chronological decomposition/combinatorial model/artificial neural networks分类
信息技术与安全科学引用本文复制引用
秦音,郭杜杜,周飞,王庆庆,王洋..零担物流时序预测的SARIMA-GRU-BPNN组合模型及应用[J].计算机工程与应用,2024,60(19):297-308,12.基金项目
新疆维吾尔自治区重点研发计划项目(2022B01015) （2022B01015）
道路交通安全公安部重点实验室开放课题基金(2023ZDSYSK-FKT06) （2023ZDSYSK-FKT06）
甘泉堡经开区科技计划项目(GKJ2023XTWL04). （GKJ2023XTWL04）
