深度学习用于时间序列异常检测：综述
Deep Learning for Time Series Anomaly Detection: A Survey
AI总结：
本文综述了基于深度学习的时间序列异常检测方法，提出分类体系并分析各类模型的优缺点、应用实例及开放挑战。
摘要：
时间序列异常检测在制造、医疗保健等广泛的研究领域和应用中具有重要用途。异常的出现可能预示着新颖或意外的事件，例如生产故障、系统缺陷或心脏颤动，因此尤其值得关注。时间序列的大规模与复杂模式促使研究人员开发专门的深度学习模型来检测异常模式。本综述重点通过深度学习提供结构化且全面的最新时间序列异常检测模型。它基于将异常检测模型划分为不同类别的因素提出了一种分类法。除描述每个类别的基本异常检测技术外，还讨论了其优点和局限性。此外，本研究还列举了近年来深度学习时间序列异常检测在多个应用领域中的实例。最后，它总结了研究中的开放问题以及在采用深度异常检测模型时所面临的挑战。
英文摘要：
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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。
