用生成模型学正常数据分布，提前发现时间序列异常
Learning Unified Representations of Normalcy for Time Series Anomaly Detection
- 基于分数驱动的生成模型学习正常数据流形
- 在多个数据集上检测准确率超越现有方法，且更早发现异常
- 适合需要早期预警的时间序列监控场景
无监督异常检测的核心挑战在于，缺乏对异常特征先验知识的情况下识别异常模式。尽管已有方法部分解决了该问题，但通常难以学习到与异常模式有明显区别的稳健正常数据分布表示。本文提出一种新框架——统一无监督异常检测（U²AD），全面应对多变量时间序列中的异常检测问题。通过基于分数的生成建模，学习正常样本的底层数据分布。我们引入一种新型时变分数网络和统一训练目标，共同刻画正常数据的流形结构，并同时考虑局部与全局时间上下文。随后通过常微分方程求解器进行确定性采样实现重构。大量实验表明，U²AD不仅在检测精度上超越当前最先进方法，还能在异常发生初期显著更早地识别出异常。
原文摘要 · Abstract (English)
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.
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