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.

翻译：在多元时间序列（MTS）数据中检测异常在许多领域都发挥着重要作用。异常值可能预示着事件、医疗异常、网络攻击或设备故障，若未及时发现，可能导致资源、资金或人命的重大损失。本文提出了一种名为贝叶斯状态空间异常检测（BSSAD）的创新性异常检测方法。BSSAD 包含两个模块：神经网络模块和贝叶斯状态空间模块。该方法的设计结合了贝叶斯状态空间算法在预测下一状态方面的优势，以及循环神经网络和自编码器在理解数据关联性方面的有效性，从而在异常检测中实现高精度。该方法的模块化设计允许灵活实现，可通过调整贝叶斯状态空间模型的参数或替换神经网络算法来达到不同性能水平。我们重点采用了粒子滤波器和集成卡尔曼滤波器两种贝叶斯状态空间模型。在五个不同数据集上进行了大量实验，结果表明，我们的模型性能优于基线模型，F1分数超过0.95。此外，我们还提出使用马修斯相关系数（MCC）这一指标，以获取关于异常检测准确性的更全面信息。