  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.
