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 Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …

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

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 …

The field of time series anomaly detection is constantly advancing, with several methods
available, making it a challenge to determine the most appropriate method for a specific …

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 …

Time series data, including univariate and multivariate ones, are characterized by unique
composition and complex multi-scale temporal variations. They often require special …

Time series anomaly detection (TSAD) has gained significant attention due to its real-world
applications to improve the stability of modern software systems. However, there is no …