摘要
物流需求是多种因素综合作用的结果,具有规律性和特殊性,变分十分复杂,导致当前物流需求预测模型的预测效果差,难以满足物流管理的实际应用要求。为了解决物流需求建模过程中存在的难题,提出基于ARIMA-SVM的物流需求预测模型。首先对当前物流需求预测的研究现状进行分析,找到引起物流需求预测效果的原因;然后选择差分自回归滑动平均模型对物流需求的规律性变化特点进行建模,支持向量机对物流需求的特殊性变化特点进行建模;最后采用权值方式确定物流需求预测的预测结果,并采用物流需求的预测实例分析模型的有效性。结果表明,ARIMA-SVM的物流需求预测结果要优于当前其他物流需求预测模型,为其他预测问题提供了一种建模工具。
The logistics demand affected by many factors has the characteristics of regularity,particularity and complex variation,which leads to the poor prediction effect of current logistics demand forecasting model,and is difficult to meet the practical application requirements of logistics management.In order to solve the problems existing in logistics demand modeling process,a logistics demand prediction model based on ARIMA-SVM is proposed.The research status of the current logistics de-mand forecasting is analyzed to find out the reason influencing the logistics demand forecasting results.The autoregressive inte-grated moving average(ARIMA)model is selected to model the regular variation characteristics of logistics demand.The sup-port vector machine(SVM)is used to model the special variation characteristics of logistics demand.The weight is used to deter-mine the prediction results of the logistics demand forecasting.The validity of the logistics demand forecasting model is analyzed by means of an example.The results show that the result of logistics demand forecasting model based on ARIMA-SVM is better than that of other logistics demand forecasting models,which provides a modeling tool for other forecasting problems.
作者
杨建成
YANG Jiancheng(Henan Institute of Technology,Xinxiang 453003,China)
出处
《现代电子技术》
北大核心
2018年第9期182-186,共5页
Modern Electronics Technique
关键词
物流管理
随机性变化特点
ARIMA-SVM
权值的确定
预测模型
支持向量机
logistics management
stochastic variation characteristic
ARIMA-SVM
weight determination
prediction model
support vector machine
作者简介
杨建成(1981—),男,河南确山人,硕士研究生,讲师。研究方向为国际贸易政策与现代物流。
