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Anomaly Detection in Multivariate Time Series Using Fuzzy AdaBoost and Dynamic Naive Bayesian Classifier

This paper presents a novel method to detect anomaly using Fuzzy AdaBoost and Dynamic Naive Bayesian classifier. Dynamic Naive Bayesian Classifier (DNBC) is an extension of Hidden Markov Models (HMM) that is used here to model multivariate observation sequences typically generated from multiple sensors associated to monitoring processes. The Fuzzy AdaBoost (FAB) method is used for ensembling multiple DNBCs to classify an instance as an anomaly. FAB needs Footprint of Uncertainty (FOU) of error that is further used to Figure out the misclassification error and update weights of the data samples required during the boosting process. Here, we introduce an approach to initialize the intervals of FOU using the statistical assets of data that belongs to the normal class. The efficacy of the proposed method is demonstrated through a case study on a stuck pipe problem that occurs during oil well drilling process.