Two-phase Outlier Detection in Multivariate Time Series

摘要：

In this paper, an efficient two-phase algorithm for detecting outlying samples in multivariate time series (MTS) datasets is proposed. The Bounded Coordinate System (BCS) metric is used to measure the similarity between two MTS samples, and the outlierness of a sample is measured by average distance to its k nearest neighbors. We partition the data into clusters, and then use nested loop algorithm to find top-n outliers. A heuristic and two pruning rules are utilized to quickly remove MTS samples that are not possible outlier candidates, reducing significantly the distance computation among objects. Experiments on two real-world datasets show the efficiency of the proposed algorithm.

关键词： Multivariate time series Bounded Coordinate System Distance-based outlier detection

作者: Xin Wang

作者单位: School of Computer Science Civil Aviation Flight University of China Guanghan 618307, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1603-1607

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）