Generative Models for Time Series Anomaly Detection: A Survey

Abstract

Time series anomaly detection (TSAD) is a fundamental practice in information management, aimed at identifying unusual patterns in temporal datasets. This process is critical to maintaining the integrity and reliability of systems. Recently, generative models have significantly advanced the capabilities of artificial general intelligence, presenting novel methodologies to understand and interpret complex data structures. In this review, we examine the latest advancements in applying generative models to TSAD and highlight how these models present a paradigm shift in detecting and analyzing anomalies within sequential data. In particular, we first present the background information, including definitions of key concepts, a taxonomy of anomaly types, and the distinction between generative and discriminative models in time series data. Then, we investigate a range of generative models, offering mathematical summaries of the predominant techniques in TSAD. Furthermore, we provide a summary of the datasets and propose recommendations for appropriate generative methods tailored to various application domains. Finally, we address the significant challenges in current research and propose potential directions for future study.