计算机应用研究2011，Vol.28Issue(7)：2466-2469,4.DOI:10.3969/j.issn.1001-3695.2011.07.017
两阶段的多元时间序列异常检测算法
Two-stage outlier detection in multivariate time series
摘要
Abstract
This paper proposed an efficient two-stage algorithm for detecting outliers in multivariate time series( MTS) data-sets. Used the bounded coordinate system (BCS) metric to measure the similarity between two MTS samples,and measured the outliemess of a sample by average distance to its ^-nearest neighbors. It partitioned the data into clusters, and used nested loop algorithm to find top-n outliers. Utilized a heuristic and two pruning rules to quickly remove MTS samples that were not possible outlier candidates, reducing significantly the distance computation among objects. Experiments on real-world datasets show the effectiveness of the proposed algorithm.关键词
多元时间序列/有界坐标系统/基于距离的异常检测Key words
multivariate time series/ bounded coordinate system/ distance-based outlier detection分类
信息技术与安全科学引用本文复制引用
王欣..两阶段的多元时间序列异常检测算法[J].计算机应用研究,2011,28(7):2466-2469,4.基金项目
国家自然科学基金资助项目(60879022,60832012) （60879022,60832012）
中国民用航空局科技项目(MHRD200801) （MHRD200801）
