面向多变量时间序列异常检测的前瞻性多图凝聚
Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
AI总结：
本文提出前瞻性多图凝聚框架PMGC，通过融合长期静态图与短期动态图并引入前瞻性构图策略，提升多变量时间序列异常检测性能。
摘要：
高维时间序列数据中的异常检测对众多工业应用至关重要。多变量时间序列异常检测（TSAD）的最新进展越来越多地利用图结构来建模变量间关系，通常采用图神经网络（GNN）。尽管现有方法取得了可观的结果，但它们往往依赖单一的图表示，不足以捕捉多变量时间序列中固有的复杂多样关系。为解决这一问题，我们提出了用于多变量TSAD的前瞻性多图凝聚（PMGC）框架。PMGC通过将长期静态图与一系列短期实例级动态图相结合来利用空间相关性，并通过图凝聚损失函数进行调控。我们的理论分析表明，该损失函数在促进动态图多样性的同时，使它们与静态图所蕴含的稳定长期关系保持一致。此外，我们引入了一种“前瞻性构图”策略，以缓解传统基于预测的TSAD方法的局限性，这些方法往往难以应对不可预测的未来变化。该策略使模型能够在正常条件下准确反映并发的序列间关系，从而提升异常检测效果。在真实世界数据集上的实证评估表明，与现有TSAD技术相比，我们的方法具有更优越的性能。
英文摘要：
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 model inter-variable relationships, typically employing Graph Neural Networks (GNNs). Despite their promising results, existing methods often rely on a single graph representation, which are insufficient for capturing the complex, diverse relationships inherent in multivariate time series. To address this, we propose the Prospective Multi-Graph Cohesion (PMGC) framework for multivariate TSAD. PMGC exploits spatial correlations by integrating a long-term static graph with a series of short-term instance-wise dynamic graphs, regulated through a graph cohesion loss function. Our theoretical analysis shows that this loss function promotes diversity among dynamic graphs while aligning them with the stable long-term relationships encapsulated by the static graph. Additionally, we introduce a "prospective graphing" strategy to mitigate the limitations of traditional forecasting-based TSAD methods, which often struggle with unpredictable future variations. This strategy allows the model to accurately reflect concurrent inter-series relationships under normal conditions, thereby enhancing anomaly detection efficacy. Empirical evaluations on real-world datasets demonstrate the superior performance of our method compared to existing TSAD techniques.
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