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Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
2021
Applied Sciences
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
doi:10.3390/app11041955
fatcat:hij6bmdmdzav7o5vhyfkcavari