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Detecting outlier samples in multivariate time series dataset

โœ Scribed by Xiaoqing Weng; Junyi Shen

Publisher
Elsevier Science
Year
2008
Tongue
English
Weight
260 KB
Volume
21
Category
Article
ISSN
0950-7051

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โœฆ Synopsis

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.

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