基于时间序列的支持向量机在物流预测中的应用

由于物流预测是不确定的、非线性的、动态开放性的复杂大系统，传统方法往往难以准确地描述这种复杂的非线性特征，因而无法准确进行物流预测，本文提出了基于一种基于时间序列的支持向量机(SVM)的物流预测方法。将该方法用于实际物流系统的公路运输量预测中，和真实值比较说明所提出的物流预测方法是可行和有效的。

Application of Support Vector Machines Based on Time Sequence in Logistics Forecasting

Because logistics system forecasting was a uncertain, nonlinear, dynamic and complicated system, it was difficult to describe such a nonlinear characteristics of this system by traditional methods, so the logistics forecasting could not be accurately forecasted. The authors presented a novel load forecasting method which is an improved support vector machines (SVM) algorithm based on time sequence applying the presented method to actual traffic forecasting of highway in logistics system, the comparison among the forecasted results and the true shows that the presented method is feasible and effective.