超越边际分布：学习多元异常检测的联合时空模式
Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection
AI总结：
本文提出通过潜在空间中解耦边际分布、时间动态与变量间依赖，并结合Transformer、copula和自监督对比学习来建模多元时间序列的联合时空模式，以提升异常检测性能。
摘要：
本文旨在通过建模多元时间序列数据中存在的时变非线性时空相关性来改进多元异常检测。在多元时间序列数据中，即使没有任何单个时间序列自身表现出明显异常模式，相互关联的时间序列同时偏离其预期集体行为也可能表明存在异常。在许多现有方法中，时间序列变量被假设为（条件）独立，这过度简化了现实世界中的相互作用。我们的方法通过在潜在空间中建模联合依赖关系，并将边际分布、时间动态和变量间依赖关系的建模解耦来解决这一问题。我们使用Transformer编码器来捕获时间模式，并通过拟合多元似然和copula来建模空间（变量间）依赖关系。时间组件和空间组件在潜在空间中使用自监督对比学习目标进行联合训练，以学习有意义的特征表示，从而区分正常样本和异常样本。
英文摘要：
In this paper, we aim to improve multivariate anomaly detection (AD) by modeling the \textit{time-varying non-linear spatio-temporal correlations} found in multivariate time series data . In multivariate time series data, an anomaly may be indicated by the simultaneous deviation of interrelated time series from their expected collective behavior, even when no individual time series exhibits a clearly abnormal pattern on its own. In many existing approaches, time series variables are assumed to be (conditionally) independent, which oversimplifies real-world interactions. Our approach addresses this by modeling joint dependencies in the latent space and decoupling the modeling of \textit{marginal distributions, temporal dynamics, and inter-variable dependencies}. We use a transformer encoder to capture temporal patterns, and to model spatial (inter-variable) dependencies, we fit a multi-variate likelihood and a copula. The temporal and the spatial components are trained jointly in a latent space using a self-supervised contrastive learning objective to learn meaningful feature representations to separate normal and anomaly samples.
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。
