Using Bayesian Network Learning Algorithm to Discover Causal Relations in Multivariate Time Series

2011 ◽  
Author(s):  
Zhenxing Wang ◽  
Laiwan Chan
Keyword(s):  
Time Series ◽  
Bayesian Network ◽  
Learning Algorithm ◽  
Multivariate Time Series ◽  
Causal Relations ◽  
Network Learning

scholarly journals Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3367 ◽  
Author(s):  
Nan Ding ◽  
Huanbo Gao ◽  
Hongyu Bu ◽  
Haoxuan Ma ◽  
Huaiwei Si
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Bayesian Network ◽  
Real Time ◽  
Multivariate Time Series ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Real ◽  
Hierarchical Temporal Memory ◽  
Sensing Time

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.

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