Forecasting intermittent demand by fuzzy support vector machines

被引:0
作者
Bao, Yukun [1 ]
Zou, Hua [1 ]
Liu, Zhitao [1 ]
机构
[1] Huazhong Univ Sci & Technol, Dept Management Sci & Informat Syst, Sch Management, Wuhan 430074, Peoples R China
来源
ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4031卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Intermittent demand appears at random, with many time periods having no demand,which is probably the biggest challenge in the repair and overhaul industry. Exponential smoothing is used when dealing with such kind of demand. Based on it, more improved methods have been studied such as Croston method. This paper proposes a novel method to forecast the intermittent parts demand based on fuzzy support vector machines (FSVM) in regression. Details on data clustering, performance criteria design, kernel function selection are presented and an experimental result is given to show the method's validity.
引用
收藏
页码:1080 / 1089
页数:10
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