Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are widely applied in MTSAD to capture the spatial features among sensors. However, GNNs depend on a graph structure and explicit graph structures are not always available. To solve the problem of missing explicit graph structure, graph structure learning is introduced to learn an accurate graph structure joint with a GNNs-based anomaly detection task. However, the existing GSL-based methods provide only a partial view of the graph structure and cannot represent multiple and complex relationships. The noise of data also brings noisy edges. Therefore, we propose a fusion graph structure learning-based multivariate time-series anomaly detection with structured prior knowledge (FuGLAD). To the best of our knowledge, it appears to be the first application of fusion graphs in time series anomaly detection. FuGLAD selects three kinds of typical graph structure learners to learn as many relationship types among sensors as possible and exploits the prior similarity to evaluate the importance of all learned graphs and adaptively learn the fusion weight instead of the direct average weight. To handle noise in raw data, FuGLAD compares the neighbors of nodes by Jaccard similarity to identify and remove the noisy edges in the prior graph. Extensive experiments demonstrate that our approach outperforms state-of-the-art single-graph structure learning techniques in detection performance across four public and real-world datasets.

中文翻译：

---

具有结构化先验知识的基于融合图结构学习的多元时间序列异常检测


多元时间序列异常检测 (MTSAD) 在物联网 (IoT) 识别设备故障或系统攻击方面发挥着至关重要的作用。图神经网络（GNN）广泛应用于 MTSAD 中来捕获传感器之间的空间特征。然而，GNN 依赖于图结构，并且显式图结构并不总是可用。为了解决缺少显式图结构的问题，引入图结构学习来学习与基于 GNN 的异常检测任务结合的准确图结构。然而，现有的基于 GSL 的方法仅提供图结构的部分视图，无法表示多种复杂的关系。数据的噪声也会带来噪声边缘。因此，我们提出了一种基于融合图结构学习的结构化先验知识的多元时间序列异常检测（FuGLAD）。据我们所知，这似乎是融合图在时间序列异常检测中的首次应用。 FuGLAD选择三种典型的图结构学习器来学习尽可能多的传感器之间的关系类型，并利用先验相似性来评估所有学习图的重要性，并自适应地学习融合权重而不是直接平均权重。为了处理原始数据中的噪声，FuGLAD 通过 Jaccard 相似度比较节点的邻居，以识别并去除先前图中的噪声边。大量的实验表明，我们的方法在四个公共和现实世界数据集的检测性能方面优于最先进的单图结构学习技术。