基于Transformer的多变量时间序列异常定位
Transformer-based Multivariate Time Series Anomaly Localization
AI总结：
本文提出基于Transformer的多变量时间序列异常定位模型，通过改进自注意力机制提升定位性能，并引入STAS和SFAS度量标准以增强异常检测的准确性。
摘要：
随着复杂度日益增加的网络物理系统（CPS）和物联网（IoT）的整合，传感器用于在线监测生成了大量多变量时间序列（MTS）数据。因此，对MTS中异常的诊断需求至关重要，以维持系统可靠性和安全性。尽管在异常检测方面取得了显著进展，但异常定位仍然是一个被广泛研究但关键的领域。本文介绍了一种新的基于Transformer的模型，用于无监督的MTS异常诊断，重点是通过深入分析正常和异常条件下自注意力机制的学习行为来提高定位性能。我们将异常定位问题制定为三个阶段的过程：时间步、窗口和段级。这导致了空间时间异常评分（STAS）的开发，这是一种受Transformer潜在表示与时空统计模型之间联系启发的新度量标准。STAS旨在捕捉个体异常行为和跨序列依赖性，提供增强的定位性能。此外，统计特征异常评分（SFAS）通过分析异常周围的统计特征来补充STAS，其组合有助于减少误报。在真实世界和合成数据集上的实验展示了该模型在检测和定位任务中优于最先进方法的优越性。
英文摘要：
With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decision-making. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the Space-Time Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and space-time statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.
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。
