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The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and heart palpitations, and is therefore of particular interest. The large size and complexity of patterns in time series data have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection. It provides a taxonomy based on anomaly detection strategies and deep learning models. Aside from describing the basic anomaly detection techniques in each category, their 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. 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