多变量时间序列异常检测通过双分支重建和自回归流基于残差密度估计
Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation
AI总结：
本文提出DBR-AF框架，通过双分支重建和自回归流模块解决多变量时间序列异常检测中的过拟合和误判问题，实验表明其在多个基准数据集上表现优异。
摘要：
多变量时间序列异常检测（MTSAD）在工业控制和航空航天系统等现实监控场景中至关重要。主流基于重建的异常检测方法存在两个关键限制：首先，过度强调跨变量建模导致伪相关性过拟合；其次，简单相加多变量重建误差生成误导性异常评分，难以区分难以重建的样本和真实异常。为解决这些问题，本文提出DBR-AF框架，整合双分支重建（DBR）编码器和自回归流（AF）模块。DBR编码器解耦跨变量相关性学习和内变量统计特性建模以缓解伪相关性，而AF模块利用多层可逆变换建模复杂的多变量残差分布，并进一步利用密度估计准确识别具有大重建误差的正常样本。在七个基准数据集上的大量实验表明，DBR-AF实现了最先进的性能，消融研究验证了其核心组件的不可或缺性。
英文摘要：
Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. Mainstream reconstruction-based anomaly detection methods suffer from two key limitations: first, overfitting to spurious correlations induced by an overemphasis on cross-variable modeling; second, the generation of misleading anomaly scores by simply summing up multivariable reconstruction errors, which makes it difficult to distinguish between hard-to-reconstruct samples and genuine anomalies. To address these issues, we propose DBR-AF, a novel framework that integrates a dual-branch reconstruction (DBR) encoder and an autoregressive flow (AF) module. The DBR encoder decouples cross-variable correlation learning and intra-variable statistical property modeling to mitigate spurious correlations, while the AF module employs multiple stacked reversible transformations to model the complex multivariate residual distribution and further leverages density estimation to accurately identify normal samples with large reconstruction errors. Extensive experiments on seven benchmark datasets demonstrate that DBR-AF achieves state-of-the-art performance, with ablation studies validating the indispensability of its core components.
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。
