Anomaly detection in multivariate time series data is an extensive field of research with significant impact on a broad spectrum of real-world applications. Building a reliable anomaly detection system is extremely challenging due to data imbalance, lack of labels, next to the actual definition of anomalies. The current study proposes a new transformer-based approach, together with a publicly available time series dataset – the TIKI data corpus – comprising various performance metrics of a Kubernetes cluster distributed over roughly 500 million timestamps. The proposed network, entitled PredTrAD, is compared with the state-of-the-art anomaly detection models – TranAD and DTAAD – in several experiments: (1) verification on three well-known benchmark corpora, (2) evaluation on the TIKI data, and (3) cross-entity anomaly detection. Across all experimental constellations, the proposed model has shown a significant performance growth, proven by F1 score enhancements of 2.41% up to 16.81%.