基于增强图注意力网络与拓扑分析的多变量时间序列异常检测
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
AI总结：
本文提出TopoGDN模型，通过多尺度时间卷积模块和融入图拓扑的增强图注意力网络，从细粒度视角分析多变量时间序列的时间与特征维度，在四个数据集上显著优于基线模型。
摘要：
时间序列的无监督异常检测在工业应用中至关重要，因为它能显著减少人工干预的需求。由于特征维度和时间维度的存在，多变量时间序列提出了复杂的挑战。传统方法使用图神经网络（GNNs）或Transformers来分析空间关系，同时使用RNNs来建模时间依赖性。这些方法狭隘地集中于单一维度或进行粗粒度的特征提取，对于具有复杂关系和动态变化的大型数据集来说是不充分的。本文提出了一种基于增强图注意力网络（GAT）构建的新型时间模型，用于多变量时间序列异常检测，称为TopoGDN。我们的模型从细粒度的角度分析所有时间和特征维度。首先，我们引入多尺度时间卷积模块来提取详细的时间特征。此外，我们提出了一种增强的GAT来管理复杂的特征间依赖关系，它将图拓扑通过多个尺度纳入节点特征中，这是一个多功能、即插即用的模块，能显著提升GAT的性能。我们的实验结果证实，我们的方法在四个数据集上超越了基线模型，展示了其在需要鲁棒异常检测的领域中具有广泛应用的潜力。代码可在 https://github.com/ljj-cyber/TopoGDN 获取。
英文摘要：
Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Multivariate time series pose a complex challenge due to their feature and temporal dimensions. Traditional methods use Graph Neural Networks (GNNs) or Transformers to analyze spatial while RNNs to model temporal dependencies. These methods focus narrowly on one dimension or engage in coarse-grained feature extraction, which can be inadequate for large datasets characterized by intricate relationships and dynamic changes. This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN. Our model analyzes both time and feature dimensions from a fine-grained perspective. First, we introduce a multi-scale temporal convolution module to extract detailed temporal features. Additionally, we present an augmented GAT to manage complex inter-feature dependencies, which incorporates graph topology into node features across multiple scales, a versatile, plug-and-play enhancement that significantly boosts the performance of GAT. Our experimental results confirm that our approach surpasses the baseline models on four datasets, demonstrating its potential for widespread application in fields requiring robust anomaly detection. The code is available at https://github.com/ljj-cyber/TopoGDN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
