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Contextual Anomaly Detection in Time Series Using Dynamic Bayesian Network

  • Achyut Mani TripathiEmail author
  • Rashmi Dutta BaruahEmail author
Conference paper
  • 284 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)

Abstract

In this paper, we propose a novel method to identify contextual anomaly in time series using Dynamic Bayesian Networks (DBN). DBN is a powerful machine learning approach that captures temporal characteristics of time series data. In order to detect contextual anomaly we integrate contextual information to the DBN framework, referred to as Contextual DBN (CxDBN). The efficacy of CxDBN is shown using a case study of the identification of contextual anomaly in real-time oil well drilling data.

Keywords

Anomaly detection Contextual anomaly Dynamic Bayesian Networks Oil well drilling 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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