学习时间序列异常检测的统一正常表示
Learning Unified Representations of Normalcy for Time Series Anomaly Detection
AI总结：
本文提出U²AD框架，通过基于分数的生成模型学习正常数据分布，结合时间依赖分数网络和统一训练目标，实现多变量时间序列异常检测的高精度与早识别。
摘要：
无监督异常检测的核心挑战是识别异常模式而无需其特征先验知识。尽管现有方法解决了此问题的部分方面，但通常难以学习与异常模式区分的正常数据分布。本文提出一种新的框架，即统一无监督异常检测（U²AD），全面解决多变量时间序列的异常检测问题。我们的方法通过利用基于分数的生成建模学习正常样本的底层数据分布。我们引入了新的时间依赖分数网络和统一训练目标，共同界定正常数据的流形，同时考虑局部和全局的时间上下文。然后通过确定性采样过程使用常微分方程求解器进行重建。我们的广泛实验评估表明，U²AD不仅在检测准确性上优于当前最先进的方法，还能在异常发生显著早期阶段识别异常。
英文摘要：
The core challenge in unsupervised anomaly detection is identifying abnormal patterns without prior knowledge of their characteristics. While existing methods have addressed aspects of this problem, they often struggle to learn a robust representation of the normal data distribution that is distinct from anomalous patterns. In this paper, we present a novel framework, Unified Unsupervised Anomaly Detection ($\text{U}^2\text{AD}$), that comprehensively addresses anomaly detection in multivariate time series. Our approach learns the underlying data distribution of normal samples by utilizing score-based generative modeling. We introduce a novel time-dependent score network and a unified training objective that together delineate the manifold of normal data while considering both local and global temporal contexts. Reconstruction is then performed via a deterministic sampling process using an ordinary differential equation solver. Our extensive experimental evaluations demonstrate that $\text{U}^2\text{AD}$ not only outperforms current state-of-the-art methods in detection accuracy but also identifies anomalies at significantly earlier stages of their occurrence.
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。
