RADM:Real-Time Anomaly Detection in Multivariate Time Series Based on Bayesian Network

Abstract

Aiming at the anomaly detection in multivariate time series(MTS), we propose a real-time anomaly detection algorithm in MTS based on Hierarchical Temporal Memory(HTM) and Bayesian Network(BN), called RADM. First of all, we use HTM model to evaluate the real-time anomalies of each univariate time series(UTS) in MTS. Secondly, a model of anomalous state detection in MTS based on Naive Bayesian is designed to analyze the validity of the above MTS. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, we utilize ternary time series of CPU utilization, Network speed and Memory occupancy ratio as data samples, and through the experimental simulation, we verify that RADM proposed in this paper can take advantage of the specific relevance in MTS and make a more effective judgment on the system anomalies.