Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Anomaly detection (AD) is a fundamental task for time-series analytics with important
implications for the downstream performance of many applications. In contrast to other …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …

Time series anomaly detection remains one of the most active areas of research in data
mining. In spite of the dozens of creative solutions proposed for this problem, recent …

Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …

In microservice systems, the identification of root causes of anomalies is imperative for
service reliability and business impact. This process is typically divided into two phases:(i) …

Time series anomaly detection is instrumental in maintaining system availability in various
domains. Current work in this research line mainly focuses on learning data normality …

With the advent of the big data era, the detection of anomalies in time series data, especially
multidimensional time series data, has received a great deal of attention from researchers in …