Self-supervised methods have gained prominence in time series anomaly detection due to
the scarcity of available annotations. Nevertheless, they typically demand extensive training …

Most of current anomaly detection models assume that the normal pattern remains the same
all the time. However, the normal patterns of web services can change dramatically and …

Unsupervised anomaly detection in multivariate time series (MTS) has always been a
challenging problem, and the modeling based on reconstruction has garnered significant …

The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is
plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking …

Anomaly detection significantly enhances the robustness of cloud systems. While neural
network-based methods have recently demonstrated strong advantages, they encounter …

Multi-class unsupervised anomaly detection aims to create a unified model for identifying
anomalies in objects from multiple classes when only normal data is available. In such a …

Slow task detection is a critical problem in cloud operation and maintenance since it is
highly related to user experience and can bring substantial liquidated damages. Most …

Time-series anomaly detection uncovers rare errors or intriguing events of interest that
significantly deviate from normal patterns. In order to precisely detect anomalies, a detector …

In large IT systems, software deployment is a crucial process in online services as their code
is regularly updated. However, a faulty code change may degrade the target service's …

In anomaly detection, methods based on large language models (LLMs) can incorporate
expert knowledge by reading professional document, while task-specific small models excel …