发表机构
Salzburg University of Applied Sciences; Paris Lodron University of Salzburg（萨尔茨堡应用科学大学; 萨尔茨堡巴黎洛德龙大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多变量时间序列异常检测，探索单变量预测基础模型TimesFM的零样本应用于工业MTSAD，评估两种策略，虽未胜过基线，但发现其在捕获时间动态上过于有效致异常难区分，不过在异常边界误差有峰值，对变化点检测有前景。
AI 中文摘要
多变量时间序列异常检测（MTSAD）对于工业过程监控和金融风险管理等领域的可靠性和安全性至关重要，但传统方法依赖特定应用模型，训练成本高且难以扩展。基础模型（FMs）在广泛数据上预训练，具有强大的零样本泛化能力，近期已用于单变量时间序列预测，引发其能否无需特定任务训练解决MTSAD的问题。我们研究了单变量预测FM（TimesFM）在安全水处理（SWaT）基准上对工业MTSAD的零样本应用，评估了两种策略：将FM视为具有阈值预测误差的按特征预测器，以及视为中间表示馈入标准离群值检测器的嵌入器。我们提出的设置均无法与既定基线竞争；嵌入仅揭示了正常和异常段之间的部分分离，不足以进行可靠检测。原因是FM在捕获时间动态方面过于有效，即使在完全异常的窗口内误差也很低，因此持续异常与正常行为难以区分。然而，这些观察提供了有价值的见解：异常边界处误差峰值表明FMs能可靠检测分布变化。我们得出结论，所提出的朴素零样本FMs不适用于MTSAD，但对变化点检测有前景。
英文摘要
Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale. Foundation Models (FMs), pre-trained on broad data with strong zero-shot generalization, have recently become available for univariate time series forecasting, raising the question of whether they can address MTSAD without task-specific training. We investigate the zero-shot application of a univariate forecasting FM, TimesFM, to industrial MTSAD on the Secure Water Treatment (SWaT) benchmark, evaluating two strategies: treating the FM as a per-feature forecaster with thresholded prediction errors, and as an embedder whose intermediate representations feed standard outlier detectors. Neither of our proposed setups is competitive with established baselines; embeddings reveal only partial separation between normal and anomalous segments, insufficient for reliable detection. The cause is that the FM is too effective at capturing temporal dynamics, yielding low error even within fully anomalous windows, so persistent anomalies become indistinguishable from normal behavior. However, these observations yield valuable insights: the error peaks at anomaly boundaries, indicating FMs reliably detect distribution changes. We conclude that the proposed naive zero-shot FMs are unsuitable for MTSAD but promising for change-point detection.
CommentsThis preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution will be published in Computer Aided Systems Theory - EUROCAST 2026, Lecture Notes in Computer Science, Springer
