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Article

Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks

1
Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
2
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
3
Lisbon ELLIS Unit (LUMLIS—Lisbon Unit for Learning and Intelligent Systems), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Lidia Jackowska-Strumillo
Appl. Sci. 2021, 11(4), 1955; https://doi.org/10.3390/app11041955
Received: 18 December 2020 / Revised: 9 February 2021 / Accepted: 12 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Machine Learning in Computer Engineering Applications)
Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many applications; however, most outlier detection methods focus solely on univariate series. We propose a complete and automatic outlier detection system covering the pre-processing of MTS data that adopts a dynamic Bayesian network (DBN) modeling algorithm. The latter encodes optimal inter and intra-time slice connectivity of transition networks capable of capturing conditional dependencies in MTS datasets. A sliding window mechanism is employed to score each MTS transition gradually, given the DBN model. Two score-analysis strategies are studied to assure an automatic classification of anomalous data. The proposed approach is first validated in simulated data, demonstrating the performance of the system. Further experiments are made on real data, by uncovering anomalies in distinct scenarios such as electrocardiogram series, mortality rate data, and written pen digits. The developed system proved beneficial in capturing unusual data resulting from temporal contexts, being suitable for any MTS scenario. A widely accessible web application employing the complete system is publicly available jointly with a tutorial. View Full-Text
Keywords: multivariate time series; outlier detection; dynamic bayesian networks; sliding window algorithm; score analysis; web application multivariate time series; outlier detection; dynamic bayesian networks; sliding window algorithm; score analysis; web application
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MDPI and ACS Style

Serras, J.L.; Vinga, S.; Carvalho, A.M. Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks. Appl. Sci. 2021, 11, 1955. https://doi.org/10.3390/app11041955

AMA Style

Serras JL, Vinga S, Carvalho AM. Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks. Applied Sciences. 2021; 11(4):1955. https://doi.org/10.3390/app11041955

Chicago/Turabian Style

Serras, Jorge L., Susana Vinga, and Alexandra M. Carvalho 2021. "Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks" Applied Sciences 11, no. 4: 1955. https://doi.org/10.3390/app11041955

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