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 …

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 …

Anomaly detection in multivariate time series is an important problem with applications in
several domains. However, the key limitation of the approaches that have been proposed so …

In recent times, transformer-based deep learning models have risen in prominence in the
field of machine learning for a variety of tasks such as computer vision and text generation …

Time series anomaly detection through unsupervised methods has been an active research
area in recent years due to its enormous potential for networks management. The …

Anomaly detection is critical to ensure cloud infrastructures' quality of service. However, due
to the complexity of inconspicuous (indistinct) anomalies, high dynamicity, and the lack of …

Time series anomaly detection is very important to ensure the security of industrial control
systems (ICSs). Many algorithms have performed well in anomaly detection. However, the …

In this paper, we propose an unsupervised anomaly detection method based on the
Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative …

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor
costs and improving processes. For instance, in rocket assembly, premature part failures can …

The detection of anomalies in dam deformation is paramount for evaluating structural
integrity and facilitating early warnings, representing a critical aspect of dam health …