计算机工程与应用2012，Vol.48Issue(20)：11-17,22,8.DOI:10.3778/j.issn.1002-8331.2012.20.003
基于距离和密度的时间序列异常检测方法研究
Research on discords detect on time series based on distance and density
摘要
Abstract
It proposes the definition of the discords detect of time series based on the representation of the GMBR (Grid Minimum Bounding Rectangle) and it is the first time to combine the distance measure method with density. It uses the "detect eigenvalue" to weigh the detect degree of the time series. Based on the proposed definition of the discords detect, it gives the new discords detect algorithm named GMBR-DD(Grid Minimum Bounding Rectangle-Discords Detect). This algorithm can find the discords time series with high-effect. It validates the definition and the proposed algorithm through three groups of the data. The experimental results show that the algorithm can catch the discords time series and the definition is reasonable. So the production provides a very effect flat roof and a powerful tool in data mining of time series.关键词
时间序列/数据挖掘/异常检测/距离/密度/符号化表示Key words
time series/ data mining/ discords detect/ distance/ density/ symbolic representation分类
信息技术与安全科学引用本文复制引用
孙梅玉..基于距离和密度的时间序列异常检测方法研究[J].计算机工程与应用,2012,48(20):11-17,22,8.基金项目
国家基金重点项目子课题(No.60825304). （No.60825304）
