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.
翻译:时间序列异常现象的探测适用于广泛的研究领域和应用,包括制造和保健;异常现象的存在可以表明新的或意外的事件,例如生产缺陷、系统缺陷或心脏发抖,因此特别令人感兴趣;由于时间序列的庞大和复杂模式,研究人员开发了用于探测异常现象模式的专门深层次学习模式;这项调查的重点是通过利用深层学习提供结构化和全面的最新时间序列异常现象探测模型;它根据将异常现象检测模型分为不同类别的各种因素提供分类;除了描述每一类别的基本异常现象探测技术外,还讨论各种优势和局限性;此外,这项研究还包括近年来在不同应用领域在时间序列中发现严重异常现象的实例;最后,它总结了在采用深度异常现象检测模型时所面临的研究和挑战方面的公开问题。