This thesis presents an unsupervised anomaly detection method for multivariate time series in an Oracle database, a relational database management system widely used in various domains. Anomaly detection is a machine learning task that aims to identify patterns in data that deviate from the expected behavior, and has applications in database performance monitoring, fraud detection, and intrusion detection. However, most existing methods require labeled data or manual selection of training data, which are not feasible in a truly unsupervised scenario. The proposed method is based on adversarially trained autoencoders, which are neural networks that learn to compress and reconstruct the input data, and can detect anomalies by measuring the reconstruction error.