An improved methodology for outlier detection in dynamic datasets | Zendy

Xu Shu | Zendy; Baldea Michael | Zendy; Edgar Thomas F. | Zendy; Wojsznis Willy | Zendy; Blevins Terrence | Zendy; Nixon Mark | Zendy

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An improved methodology for outlier detection in dynamic datasets

Author(s) -

Xu Shu,

Baldea Michael,

Edgar Thomas F.,

Wojsznis Willy,

Blevins Terrence,

Nixon Mark

Publication year - 2015

Publication title -

aiche journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.958

H-Index - 167

eISSN - 1547-5905

pISSN - 0001-1541

DOI - 10.1002/aic.14631

Subject(s) - outlier , anomaly detection , univariate , kalman filter , computer science , process (computing) , data mining , series (stratigraphy) , filter (signal processing) , multivariate statistics , line (geometry) , artificial intelligence , pattern recognition (psychology) , machine learning , mathematics , computer vision , paleontology , geometry , biology , operating system

A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier and innovational outlier detection problems in dynamic process dataset. Compared with current outlier detection methods, the new method enjoys the following advantages: (a) no prior knowledge of the process model is needed; (b) it is easy to tune; (c) it can be applied to both univariate and multivariate outlier detection; (d) it is applicable to both on‐line and off‐line operation; (e) it cleans outliers while maintains the integrity of the original dataset. © 2014 American Institute of Chemical Engineers AIChE J , 61: 419–433, 2015

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