Uni-directional graph structure learning-based multivariate time series anomaly detection with dynamic prior knowledge

In the Internet of Things (IoT) system, sensors generate a vast amount of multivariate time series data and transmit it to the data center for aggregation and analysis. However, due to equipment failure or attacks, the collected data may contain anomalies, which in turn affect the overall performance and reliability of IoT services. Therefore, an effective multivariate time series anomaly detection (MTSAD) method is a crucial issue to ensure the quality of service. Graph structure learning (GSL)-based methods become a promising technology in MTSAD, which learns an optimal graph structure joint with the anomaly detection task. However, most existing methods disregard the causal and dynamic relationships between sensors during the processing of IoT and assume that the data is devoid of any missing values. Therefore, we propose a uni-direction graph structure learning-based multivariate time-series anomaly detection with dynamic prior knowledge (DPGLAD), which learns the uni-directional relationships between sensors under the constraint of the dynamic prior graph and utilizes diffusion convolutional recurrent neural networks (DCRNN) based on timestamp mask to extract temporal and spatial features. Extensive experiments show that our method has better detection performance and shorter training times than state-of-the-art techniques on four real-world datasets. Compared with the best GSL-based method GTA, DPGLAD achieves 4.16–7.29% more F1-score.