无需训练的时序异常检测方法，通过二维依赖建模提升精度
VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection
- 用STL分解时序为趋势、季节和残差分量，分离噪声
- 将一维时序转为二维相关矩阵，捕捉复杂时间依赖关系
- 无需训练，适合工业部署，五大基准上表现领先
时序异常检测对发现未标记数据中的罕见有害事件至关重要。现有方法高度依赖高质量输入，易受噪声影响；且传统一维表示难以充分捕捉时序中复杂的依赖关系。本文提出VISTA，一种无需训练的无监督时序异常检测算法。其包含三个核心模块：1）基于局部加权回归季节分解（STL）的时序分解，将噪声时序分解为趋势、季节和残差成分；2）时间自注意力机制，将一维时序转化为二维时序相关矩阵，实现更丰富的依赖建模与异常检测；3）多变量时序聚合，利用预训练特征提取器整合跨变量信息，生成统一且内存高效的表示。VISTA无需训练，支持快速部署和简单调参，适用于工业场景，在五个多变量时序异常检测基准上达到当前最优性能。
原文摘要 · Abstract (English)
Time Series Anomaly Detection (TSAD) is essential for uncovering rare and potentially harmful events in unlabeled time series data. Existing methods are highly dependent on clean, high-quality inputs, making them susceptible to noise and real-world imperfections. Additionally, intricate temporal relationships in time series data are often inadequately captured in traditional 1D representations, leading to suboptimal modeling of dependencies. We introduce VISTA, a training-free, unsupervised TSAD algorithm designed to overcome these challenges. VISTA features three core modules: 1) Time Series Decomposition using Seasonal and Trend Decomposition via Loess (STL) to decompose noisy time series into trend, seasonal, and residual components; 2) Temporal Self-Attention, which transforms 1D time series into 2D temporal correlation matrices for richer dependency modeling and anomaly detection; and 3) Multivariate Temporal Aggregation, which uses a pretrained feature extractor to integrate cross-variable information into a unified, memory-efficient representation. VISTA's training-free approach enables rapid deployment and easy hyperparameter tuning, making it suitable for industrial applications. It achieves state-of-the-art performance on five multivariate TSAD benchmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。