  Detecting anomalies in multivariate time series(MTS) data plays an important
role in many domains. The abnormal values could indicate events, medical
abnormalities,cyber-attacks, or faulty devices which if left undetected could
lead to significant loss of resources, capital, or human lives. In this paper,
we propose a novel and innovative approach to anomaly detection called Bayesian
State-Space Anomaly Detection(BSSAD). The BSSAD consists of two modules: the
neural network module and the Bayesian state-space module. The design of our
approach combines the strength of Bayesian state-space algorithms in predicting
the next state and the effectiveness of recurrent neural networks and
autoencoders in understanding the relationship between the data to achieve high
accuracy in detecting anomalies. The modular design of our approach allows
flexibility in implementation with the option of changing the parameters of the
Bayesian state-space models or swap-ping neural network algorithms to achieve
different levels of performance. In particular, we focus on using Bayesian
state-space models of particle filters and ensemble Kalman filters. We
conducted extensive experiments on five different datasets. The experimental
results show the superior performance of our model over baselines, achieving an
F1-score greater than 0.95. In addition, we also propose using a metric called
MatthewCorrelation Coefficient (MCC) to obtain more comprehensive information
about the accuracy of anomaly detection.
