Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance
•Developed simultaneous detection of multiple change points.•Handled change point detection in presence of unknown and varying covariance.•Solved change detection problem under Expectation Maximization framework. Data analysis plays an important role in system modeling, monitoring and optimization....
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| Published in | Computers & chemical engineering Vol. 69; pp. 128 - 146 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
03.10.2014
Elsevier |
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| Abstract | •Developed simultaneous detection of multiple change points.•Handled change point detection in presence of unknown and varying covariance.•Solved change detection problem under Expectation Maximization framework.
Data analysis plays an important role in system modeling, monitoring and optimization. Among those data analysis techniques, change point detection has been widely applied in various areas including chemical process, climate monitoring, examination of gene expressions and quality control in the manufacturing industry, etc. In this paper, an Expectation Maximization (EM) algorithm is proposed to detect the time instants at which data properties are subject to change. The problem is solved in the presence of unknown and changing mean and covariance in process data. Performance of the proposed algorithm is evaluated through simulated and experimental study. The results demonstrate satisfactory detection of single and multiple changes using EM approach. |
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| AbstractList | Data analysis plays an important role in system modeling, monitoring and optimization. Among those data analysis techniques, change point detection has been widely applied in various areas including chemical process, climate monitoring, examination of gene expressions and quality control in the manufacturing industry, etc. In this paper, an Expectation Maximization (EM) algorithm is proposed to detect the time instants at which data properties are subject to change. The problem is solved in the presence of unknown and changing mean and covariance in process data. Performance of the proposed algorithm is evaluated through simulated and experimental study. The results demonstrate satisfactory detection of single and multiple changes using EM approach. •Developed simultaneous detection of multiple change points.•Handled change point detection in presence of unknown and varying covariance.•Solved change detection problem under Expectation Maximization framework. Data analysis plays an important role in system modeling, monitoring and optimization. Among those data analysis techniques, change point detection has been widely applied in various areas including chemical process, climate monitoring, examination of gene expressions and quality control in the manufacturing industry, etc. In this paper, an Expectation Maximization (EM) algorithm is proposed to detect the time instants at which data properties are subject to change. The problem is solved in the presence of unknown and changing mean and covariance in process data. Performance of the proposed algorithm is evaluated through simulated and experimental study. The results demonstrate satisfactory detection of single and multiple changes using EM approach. |
| Author | Huang, Biao Keshavarz, Marziyeh |
| Author_xml | – sequence: 1 givenname: Marziyeh surname: Keshavarz fullname: Keshavarz, Marziyeh – sequence: 2 givenname: Biao surname: Huang fullname: Huang, Biao email: biao.huang@ualberta.ca |
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| Cites_doi | 10.1016/j.compchemeng.2013.09.012 10.1080/03610920701827581 10.1093/biomet/62.2.407 10.1016/0167-7152(88)90118-6 10.1016/j.jprocont.2007.09.003 10.1080/01621459.1993.10594323 10.1016/S0167-9473(00)00068-2 10.1016/j.csda.2009.09.003 10.1007/BF00054787 10.1080/01621459.1997.10473616 10.1007/s004770000051 10.1016/j.jprocont.2007.06.002 10.1111/j.2517-6161.1977.tb01600.x 10.1080/01621459.1986.10478260 10.1016/j.jprocont.2010.11.008 10.1198/004017006000000291 10.1016/0167-9473(96)00007-2 10.1214/aoms/1177700517 10.1007/s10986-006-0028-9 10.1080/10618600.2012.674653 10.1214/aos/1176343001 10.1016/j.cor.2006.02.018 10.1111/j.1467-9876.2004.05155.x 10.1016/S0022-1694(00)00270-5 10.1016/j.csda.2004.03.003 10.1016/S0167-9473(02)00177-9 10.1093/biomet/57.1.1 |
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| Keywords | Bayesian inference Change point detection Expectation Maximization Bayes estimation Data analysis Event detection Time series Change point Experimental study Modeling Optimization Data acquisition system Covariance regime switching model EM algorithm System monitoring |
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| SubjectTerms | Algorithms Applied sciences Bayesian inference Change point detection Chemical engineering Computer science; control theory; systems Computer simulation Control system analysis Control theory. Systems Covariance Data processing Exact sciences and technology Expectation Maximization Mathematics Maximization Monitoring Optimization Parametric inference Probability and statistics Sciences and techniques of general use Statistics |
| Title | Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance |
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