用分块重构误差检测时间序列异常，效率高且效果佳。
PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection
- 将时间序列分块，用Transformer重建块内数据
- 在多个基准数据集上达到顶尖检测性能
- 适合需要实时监控的工业场景
时间序列异常检测（TSAD）旨在识别流式数据中显著偏离正常模式的观测。随着物联网设备普及，时间序列异常检测愈发重要，可实现实时监控与早期异常发现。本文提出PatchTrAD，一种基于分块的Transformer模型，通过重建框架实现时间序列异常检测。该方法利用Transformer编码器对分块数据进行重建，并以重建误差作为异常判别依据。在多个基准数据集上的实验表明，PatchTrAD在检测性能上媲美当前最优深度学习模型，同时推理速度更快，具备实际部署优势。
原文摘要 · Abstract (English)
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.
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。