Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition, or medical diagnosis. Several algorithms have been introduced using ...

which means that there is still room for further improvement in anomaly detection and early diagnosis. SCADA data, which is essentially complex and multivariate time-series data closely related to the ...

The neural autoencoder anomaly detection technique presented in this article is just one of many ways to look for data anomalies. The technique assumes you are working with tabular data, such as log ...

Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that ...

Further, existing studies on anomaly detection for multivariate time series focus solely on the approach without examining its challenges. A group of researchers from Chung-Ang University in Korea ...