Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

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

Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …

Anomaly detection in time series is a complex task that has been widely studied. In recent
years, the ability of unsupervised anomaly detection algorithms has received much attention …

The recent wave of digitalization is characterized by the widespread deployment of sensors
in many different environments, eg, multi-sensor systems represent a critical enabling …

Industrial cyber-physical systems (ICPSs) play an important role in many critical
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

In recent years, specific evaluation metrics for time series anomaly detection algorithms
have been developed to handle the limitations of the classical precision and recall …

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 …

Time series anomaly detection is challenging due to the complexity and variety of patterns
that can occur. One major difficulty arises from modeling time-dependent relationships to …