Graph-based Time-Series Anomaly Detection: A Survey and Outlook

arXiv:2302.00058v3 Announce Type: replace
Abstract: With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in …

abstract advances anomaly anomaly detection applications arxiv commerce cs.lg cybersecurity data detection e-commerce generate graph graph-based healthcare maintenance monitoring outlook series survey systems technology time series type