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On-line outlier and change point detection for time series
Su WX(苏卫星); Zhu YL(朱云龙); Liu F(刘芳); Hu KY(胡琨元)
Indexed BySCI ; EI
EI Accession number20132016322997
WOS IDWOS:000313367900016
Contribution Rank1
KeywordOutlier Detection Change Point Detection Time Series Hypothesis Test
AbstractThe detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.
WOS HeadingsScience & Technology ; Technology
WOS SubjectMetallurgy & Metallurgical Engineering
WOS Research AreaMetallurgy & Metallurgical Engineering
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorSu WX(苏卫星)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Graduate School of Chinese Academy of Sciences, Beijing 100039, China
3.School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Recommended Citation
GB/T 7714
Su WX,Zhu YL,Liu F,et al. On-line outlier and change point detection for time series[J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY,2013,20(1):114-122.
APA Su WX,Zhu YL,Liu F,&Hu KY.(2013).On-line outlier and change point detection for time series.JOURNAL OF CENTRAL SOUTH UNIVERSITY,20(1),114-122.
MLA Su WX,et al."On-line outlier and change point detection for time series".JOURNAL OF CENTRAL SOUTH UNIVERSITY 20.1(2013):114-122.
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