A. Pruden

VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data

Time-series anomaly detection holds value across various research fields and application domains, serving purposes such as fault diagnosis, identification of unexpected system intrusions, or signaling the onset of new disease outbreaks. To identify anomalous timepoints in a practical manner, unsupervised learning metho...