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ARMA-BP物流需求预测模型及应用 被引量:1

ARMA-BP Logistics Demand Forecasting Model and Its Application
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摘要 基于物流需求的实时性和不确定性,提出融合时间序列自回归-滑动平均模型ARMA和BP神经网络,构建了物流需求预测ARMA-BP模型,提出预测货物运输的ARMA-BP结合预测算法。以唐山市近几年物流运输数据为研究对象,分别运用ARMA模型、BP模型和ARMA-BP模型对物流数据进行预测分析,结果表明,与传统预测模型相比,ARMA-BP模型预测精度更高,具有一定的实用价值。 Based on the real time and uncertainty of logistics demand,the fusion time series autoregressivemoving average model ARMA and BP Neural network were proposed,and the logistics demand forecasting ARMA-BP model was modelled,An ARMA-BP combined prediction algorithm for predicting freight transport was proposed.Based on the logistics transportation data of Tangshan in recent years,using ARMA model,BP model and ARMA-BP model,the logistics data were predicted.The results show that the ARMA-BP model has higher precision and practical value than the traditional prediction model.
作者 刘凤春 赵亚宁 董新雁 刘源铄 乔鹏 谢志远 王立亚 张春英 LIU Feng-chun 1, ZHAO Ya-ning 2, DONG Xin-yan 2, LIU Yuan-shuo 2, QIAO Peng 2, XIE Zhi-yuan 2, WANG Li-ya 2, ZHANG Chun-ying 2(1.Qian College, North China University of Science and Technology, Tangshan Hebei 063000, China; 2.College of Sciences, North China University of Science and Technology, Tangshan Hebei 063210, Chin)
出处 《华北理工大学学报(自然科学版)》 CAS 2018年第3期120-128,共9页 Journal of North China University of Science and Technology:Natural Science Edition
基金 河北省自然科学基金资助(F2016209344 F2018209374)
关键词 时间序列 神经网络 残差序列 物流需求预测 time series neural network residual error sequence logistics demand forecasting
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