Anomaly detection within multivariate time series (MTS) is an essential task in both data
mining and service quality management. Many recent works on anomaly detection focus on …

Anomaly detection on multivariate time-series is of great importance in both data mining
research and industrial applications. Recent approaches have achieved significant progress …

The rapid development of smart factories, combined with the increasing complexity of
production equipment, has resulted in a large number of multivariate time series that can be …

Anomaly detection on multivariate time series (MTS) is of great importance in both data
mining research and industrial applications. While a handful of anomaly detection models …

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

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 …

In many complex systems, devices are typically monitored and generating massive
multivariate time series. However, due to the complex patterns and little useful labeled data …

Detecting anomalies in real-world multivariate time series data is challenging due to
complex temporal dependencies and inter-variable correlations. Recently, reconstruction …

The majority of existing anomaly detection methods for multivariate time series are based on
Transformers and Autoencoders owing to their superior capabilities. However, these …

Nowadays, multivariate time series data are increasingly collected in various real world
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …