Detection of Outlier Samples in Multivariate Time Series
WENG XiaoQing1, 2, SHEN JunYi1
1.Institute of Computer Software, Xi’an Jiaotong University, Xi’an 710049 2.Computer Center, Hebei University of Economics and Trade, Shijiazhuang 050061
Abstract：Multivariate time series (MTS) datasets are commonly used in the fields of finance, multimedia and medicine. MTS samples, namely outlier samples, are significantly different from the other MTS samples. In this paper, a method for detecting outlier samples in the MTS dataset based on local sparsity coefficient is proposed. An extended Frobenius norm is used to compare the similarity between two MTS samples, and knearest neighbor (kNN) searches are performed by using twophase sequential scan. MTS samples that are not possible outlier candidates are pruned, which reduces the number of computations and comparisons. Experiments are carried out on two realworld datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experimental results show the effectiveness of the proposed method.
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2007, Vol. 20 
