The detection of anomalies in multivariate time-series data is becoming increasingly
important in the automated and continuous monitoring of complex systems and devices due …

In the real world, a large number of multivariate time series data are generated by Internet of
Things systems, which are composed of many connected sensing devices. Therefore, it is …

Graph neural networks have been widely used by multivariate time series-based anomaly
detection algorithms to model the dependencies of system sensors. Previous studies have …

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-
variate time series data, which are of major significance for today's industrial applications …

Real-world industrial systems contain a large number of interconnected sensors that
generate a significant amount of time series data during system operation. Performing …

Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-
world application domains. Over the past few years, MTAD has attracted rapidly increasing …

As a major category of unsupervised anomaly detection methods for multivariate time series,
autoregression-based methods train a predictor to model the normal pattern from only …

Monitoring complex systems results in massive multivariate time series data, and anomaly
detection of these data is very important to maintain the normal operation of the systems …

Anomaly detection in multivariate time series is a critical research area, but it is also a
challenging one due to its occurrence in various real-world scenarios, such as structural …

The high complexity and wide applications of cyber–physical systems (CPSs) pose a large
requirement on both accuracy and interpretability of the time-series anomaly detection …