PatchTrAD：一种聚焦于分块重构误差的基于分块的Transformer时间序列异常检测模型
PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection
AI总结：
本文提出PatchTrAD，一种在重构框架下结合分块与Transformer编码器的时间序列异常检测模型，在多个基准上达到与前沿深度学习模型相当的检测性能，并提升推理效率。
摘要：
时间序列异常检测（TSAD）专注于识别流数据中的观测值是否显著偏离正常模式。随着互联设备的普及，时间序列异常检测变得至关重要，因为它能够在各种应用领域中实现实时监控和异常行为的早期发现。在本研究中，我们提出了PatchTrAD，这是一种用于时间序列异常检测的基于分块（Patch）的Transformer模型。我们的方法在基于重构的异常检测框架下，利用Transformer编码器并结合分块机制。在多个基准数据集上的实证评估表明，PatchTrAD在检测性能方面与最先进的深度学习异常检测模型相当，同时在推理过程中具有较高的时间效率。
英文摘要：
Time series anomaly detection (TSAD) focuses on identifying whether observations in streaming data deviate significantly from normal patterns. With the prevalence of connected devices, anomaly detection on time series has become paramount, as it enables real-time monitoring and early detection of irregular behaviors across various application domains. In this work, we introduce PatchTrAD, a Patch-based Transformer model for time series anomaly detection. Our approach leverages a Transformer encoder along with the use of patches under a reconstructionbased framework for anomaly detection. Empirical evaluations on multiple benchmark datasets show that PatchTrAD is on par, in terms of detection performance, with state-of-the-art deep learning models for anomaly detection while being time efficient during inference.
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。
