Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

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 …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Despite the great advances made by deep learning in many machine learning problems,
there is a relative dearth of deep learning approaches for anomaly detection. Those …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection
systems are needed to lessen the monitoring burden placed on operations engineers and …

To ensure undisrupted business, large Internet companies need to closely monitor various
KPIs (eg, Page Views, number of online users, and number of orders) of its Web …

Time series anomalies can offer information relevant to critical situations facing various
fields, from finance and aerospace to the IT, security, and medical domains. However …