摘要：

随着多变量时序数据在各行业中的广泛应用, 开发有效的异常检测方法对于保障系统的稳定运行和安全性变得极为关键, 由于多变量时序数据内在的复杂性和动态变化特性, 对异常检测算法提出了更高的要求。针对现有异常检测方法在处理含有复杂变量关系的高维数据时存在效率不足的问题, 提出一种基于图神经网络(GNN)与扩散模型的多变量时序数据异常检测算法GRD。通过节点嵌入和图结构学习, GRD算法能有效地捕捉和表示变量间的复杂关系, 并通过门控循环单元(GRU)和去噪扩散概率模型(DDPM)进一步提取特征, 实现了对异常数据的高精度检测。在以往的实验评估中, 大多数算法在评分前会采用点调整(PA)评估协议, 该协议会严重高估算法的检测能力。为了更准确地评估算法性能, 采用新的评估协议和评价指标。实验结果表明, GRD算法在3个公开数据集上的F1@k指标分别是0.741 4、0.801 7、0.767 1, 性能优于现有方法。特别是在高维数据处理方面, GRD算法展现出显著优势, 证明了其在现实场景的异常检测应用中的实用性和鲁棒性。

关键词: 多变量时序数据, 异常检测, 图神经网络, 扩散模型, 评估协议

Abstract:

As multivariate time series data become increasingly prevalent across various industries, anomaly detection methods that can ensure the stable operation and security of systems have become crucial. Owing to the inherent complexity and dynamic nature of multivariate time series data, higher demands are placed on anomaly detection algorithms. To address the inefficiencies of existing anomaly detection methods in processing high-dimensional data with complex variable relations, this study proposes an anomaly detection algorithm for multivariate time series data, based on Graph Neural Networks (GNNs) and a diffusion model, named GRD. By leveraging node embedding and graph structure learning, GRD algorithm proficiently captures the relations between variables and refines features through a Gated Recurrent Unit (GRU) and a Denoising Diffusion Probabilistic Model (DDPM), thereby facilitating precise anomaly detection. Traditional assessment methods often employ a Point-Adjustment (PA) protocol that involves pre-scoring, substantially overestimating an algorithm's capability. To reflect model performance realistically, this work adopts a new evaluation protocol along with new metrics. The GRD algorithm demonstrates F1@k scores of 0.741 4, 0.801 7, and 0.767 1 on three public datasets. These results indicate that GRD algorithm consistently outperforms existing methods, with notable advantages in the processing of high-dimensional data, thereby underscoring its practicality and robustness in real-world anomaly detection applications.

Key words: multivariate time series data, anomaly detection, Graph Neural Network (GNN), diffusion model, evaluation protocol