Time-Series Anomaly Detection: Overview and New Trends

Abstract

Anomaly detection is a fundamental data analytics task across scientific fields and industries. In recent years, an increasing interest has been shown in the application of anomaly detection techniques to time series. In this tutorial, we take a holistic view of anomaly detection in time series and comprehensively cover detection algorithms ranging from the 1980s to the most current state-of-the-art techniques. Importantly, the scope of this tutorial extends beyond algorithmic discussion, delving into the latest advancements in benchmarking and evaluation measures for this area. In particular, our interactive systems enable the exploration of detection algorithms and benchmarking results, thereby promoting user comprehension. Driven by the absence of a one-size-fits-all anomaly detector for various time series domains and applications, we review recent advancements in automated solutions and propose a new taxonomy to motivate further research.