Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
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.
Code (1)
Tasks
Anomaly DetectionGraph AttentionTime SeriesTime Series Anomaly DetectionUnsupervised Anomaly DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series da…
Anomaly DetectionTime SeriesTime Series Anomaly DetectionProspective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have increasingly leveraged graph structures to…
Time Series Anomaly DetectionMoon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection
Multivariate time series (MTS) anomaly detection identifies abnormal patterns where each timestamp contains multiple variables. Existing MTS anomaly detection methods fall into three categories: reconstruction-based, pre…
Time Series Anomaly DetectionGenAD: General Representations of Multivariate Time Series for Anomaly Detection
Anomaly Detection(AD) for multivariate time series is an active area in machine learning, with critical applications in Information Technology system management, Spacecraft Health monitoring, Multi-Robot Systems detectio…
Anomaly DetectionManagementTime SeriesTime Series Analysis+1Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QG…
Anomaly DetectionGenerative Adversarial NetworkTime SeriesTime Series Anomaly Detection