多变量时间序列异常样本的识别<sup>*</sup>

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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 knearest neighbor (kNN) searches are performed by using twophase 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 realworld datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experimental results show the effectiveness of the proposed method.

Key words： Multivariate Time Series (MTS) Local Sparsity Coefficient Extended Frobenius Norm Outlier Sample

Received: 22 November 2005

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	WENG XiaoQing
	SHEN JunYi

Cite this article:

WENG XiaoQing,SHEN JunYi. Detection of Outlier Samples in Multivariate Time Series[J]. , 2007, 20(4): 463-468.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I4/463

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