基于时空图条件扩散模型的受污染多变量时间序列异常检测
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
AI总结：
针对训练数据被异常污染的无监督时间序列异常检测问题，提出TSAD-C框架，通过去污染、长程变量依赖建模和异常评分三模块实现端到端检测，并在四个数据集上达到新SOTA。
摘要：
主流无监督异常检测算法通常在学术数据集上表现优异，但由于涉及干净训练数据的受控实验条件，其在真实世界中的性能受到限制。应对带噪训练这一实际异常检测中普遍存在的挑战，常常被忽视。作为一项开创性尝试，本研究深入探讨了感官时间序列异常检测（TSAD）中的标签级噪声领域。本文提出了一种新颖且实用的端到端无监督TSAD方法，适用于训练数据被异常污染的情况。所引入的方法称为TSAD-C，在训练阶段无法获取异常标签。TSAD-C包含三个核心模块：用于纠正训练期间存在的异常（也称噪声）的Decontaminator（去污染器）；用于在去污染数据（被视为纯净正常数据的替代）中捕获长期变量内和变量间依赖关系的Long-range Variable Dependency Modeling（长程变量依赖建模）模块；以及用于检测所有类型异常的Anomaly Scoring（异常评分）模块。我们在四个可靠且多样的数据集上进行的大量实验确凿表明，TSAD-C超越了现有方法，从而在TSAD领域确立了新的最先进水平。
英文摘要：
Mainstream unsupervised anomaly detection algorithms often excel in academic datasets, yet their real-world performance is restricted due to the controlled experimental conditions involving clean training data. Addressing the challenge of training with noise, a prevalent issue in practical anomaly detection, is frequently overlooked. In a pioneering endeavor, this study delves into the realm of label-level noise within sensory time-series anomaly detection (TSAD). This paper presents a novel and practical end-to-end unsupervised TSAD when the training data is contaminated with anomalies. The introduced approach, called TSAD-C, is devoid of access to abnormality labels during the training phase. TSAD-C encompasses three core modules: a Decontaminator to rectify anomalies (aka noise) present during training, a Long-range Variable Dependency Modeling module to capture long-term intra- and inter-variable dependencies within the decontaminated data that is considered as a surrogate of the pure normal data, and an Anomaly Scoring module to detect anomalies from all types. Our extensive experiments conducted on four reliable and diverse datasets conclusively demonstrate that TSAD-C surpasses existing methodologies, thus establishing a new state-of-the-art in the TSAD field.
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。
