针对产品销售时序具有小样本、含噪声的数据特征，本文设计了一种迭代的支持向量机模型(Iε-SVM)。Iε-SVM采用迭代的方式，在SVM中参数ε逐步减小的过程中，一步步修正那些可能受噪声影响较大的样本点信息，降低这些样本点对最终生成的预测模型的影响。Iε-SVM与ε-SVM一起被应用于处理一个数值算例和一个汽车销售预测实例中，仿真实验结果表明Iε-SVM是有效可行的，可获得比ε-SVM更精确的预测结果。
Aiming at data characteristics of small samples and noise existing in the product sale series, an iterative support vector machine (Iε-SVM) is proposed in this paper. During the gradually reducing process of Iε-SVM’s parameter ε, the samples greatly affected by noise are iteratively amended to reduce their influence on the final forecasting model generated. Iε-SVM is applied to a numerical value example and the automobile sales forecasting in contrast with the ε support vector machine (ε-SVM). The experiment results indicate that Iε-SVM is effective and feasible, by which more accurate forecasting results are obtained over the ε-SVM.