基于支持向量回归的服务备件需求量预测研究

Research of Spare Parts Requirement Prediction Based on Support Vector Regression

随着市场竞争的日益激烈,售后维修服务成为一种有效的竞争手段。作为维修服务物质基础的备件,在服务中具有决定性的作用,所以服务备件物流管理在实践上得到越来越多的重视。本文将基于支持向量回归的数据挖掘方法,用于服务备件需求预测研究中。并结合实例,讨论了支持向量回归在汽车维修服务备件需求预测中的应用及其特点。

Owing to the fierce competition of the market, after service is becoming an effective means of competition. As the foundation of after service, spare parts play a vital role. Spare parts logistics get more and more emphasis. This paper applies a new data mining method based on SVR （support vector regression） in the prediction of the spare parts requirement. And combining with the example, discusses the application and features of SVR in the prediction of the spare parts requirement.