Detecting outlier samples in multivariate time series dataset

Authors:
Xiaoqing Weng;Junyi Shen
Affiliations:
Institute of Computer Software, Xi'an Jiaotong University, Xi'an 710049, China and Computer Center of Hebei University of Economics and Trade, Shijiazhuang 050061, China;Institute of Computer Software, Xi'an Jiaotong University, Xi'an 710049, China
Venue:
Knowledge-Based Systems
Year:
2008

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Abstract

Multivariate time series (MTS) samples which differ significantly from other MTS samples are referred to as outlier samples. In this paper, an algorithm designed to efficiently detect the top n outlier samples in MTS dataset, based on Solving Set, is proposed. An extended Frobenius Norm is used to compute the distance between MTS samples. The outlier score of MTS sample is the sum of the distances from its k nearest neighbors. The time complexity of the algorithm is subquadratic. We conduct experiments on two real-world datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experiment results show the efficiency and effectiveness of the algorithm.