Multivariate time series (MTS) anomaly detection is a critical task that involves identifying abnormal patterns or events in data that consist of multiple interrelated time series. In order to better model the complex interdependence between entities and the various inherent characteristics of each entity, the GNN based methods are widely adopted by existing methods. In each layer of GNN, node features aggregate information from their neighboring nodes to update their information. In doing so, from shallow layer to deep layer in GNN, original individual node features continue to be weakened and more structural information,i.e., from short-distance neighborhood to long-distance neighborhood, continues to be enhanced. However, research to date has largely ignored the understanding of how hierarchical graph information is represented and their characteristics that can benefit anomaly detection. Existing methods simply leverage the output from the last layer of GNN for anomaly estimation while neglecting the essential information contained in the intermediate GNN layers. To address such limitations, in this paper, we propose a Graph Mixture of Experts (Graph-MoE) network for multivariate time series anomaly detection, which incorporates the mixture of experts (MoE) module to adaptively represent and integrate hierarchical multi-layer graph information into entity representations. It is worth noting that our Graph-MoE can be integrated into any GNN-based MTS anomaly detection method in a plug-and-play manner. In addition, the memory-augmented routers are proposed in this paper to capture the correlation temporal information in terms of the global historical features of MTS to adaptively weigh the obtained entity representations to achieve successful anomaly estimation. Extensive experiments on five challenging datasets prove the superiority of our approach and each proposed module.

翻译：多元时间序列异常检测是一项关键任务，旨在识别由多个相互关联的时间序列组成的数据中的异常模式或事件。为了更好地建模实体间复杂的相互依赖关系以及每个实体的多种内在特征，现有方法广泛采用基于图神经网络的方法。在图神经网络的每一层中，节点特征通过聚合来自相邻节点的信息来更新自身信息。在此过程中，从图神经网络的浅层到深层，原始个体节点特征持续被削弱，而更多结构信息（即从短距离邻域到长距离邻域的信息）持续增强。然而，迄今为止的研究在很大程度上忽略了对层次化图信息如何表征及其有益于异常检测的特征的理解。现有方法仅简单利用图神经网络最后一层的输出来进行异常估计，而忽视了中间图神经网络层所包含的重要信息。为应对这些局限性，本文提出了一种用于多元时间序列异常检测的图混合专家网络，该网络结合混合专家模块，自适应地表征并将层次化多层图信息整合到实体表示中。值得注意的是，我们的图混合专家模块能够以即插即用的方式集成到任何基于图神经网络的多元时间序列异常检测方法中。此外，本文提出了记忆增强路由器，通过捕获多元时间序列全局历史特征中的相关时序信息，自适应地对获得的实体表示进行加权，从而实现成功的异常估计。在五个具有挑战性的数据集上进行的大量实验证明了我们方法及其各提出模块的优越性。