Demand Forecasting of Supply Chain Based on Support Vector Regression Method

被引：14
|
作者：
Wang Guanghui [1 ]
机构：
[1] S China Univ Technol, Sch Business Adm, Guangzhou 510641, Peoples R China
来源：
2012 INTERNATIONAL WORKSHOP ON INFORMATION AND ELECTRONICS ENGINEERING | 2012年 / 29卷
关键词：
Support vector regression; Supply Chain; Demand Forecasting;
D O I：
10.1016/j.proeng.2011.12.707
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Introducing the basic theory and computing process of time series forecasting based on Support Vector Regression (SVR) in details, optimizing the parameters of SVR by Genetic Algorithm (GA). Applying SVR to forecast the demand of supply chain in real data, and compared to the RBF neural network method. The result shows that SVR is superior to RBF in prediction performance. And SVR is the suitable and effective method for demand forecasting of supply chain. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology
引用
收藏
页码：280 / 284
页数：5
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