Dive into Time-Series Anomaly Detection: A Decade Review
It provides a comprehensive review for researchers and practitioners in fields like cybersecurity and healthcare, but it is incremental as it synthesizes existing literature without introducing new methods.
This survey tackles the need for a structured overview of time-series anomaly detection methods by grouping existing solutions under a process-centric taxonomy and performing a meta-analysis to outline research trends.
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.