Abstract: This disclosure is directed to automated computer-implemented methods and systems for runtime detection and reporting of anomalous behavior objects running in a data center. A deep neural network is trained to generated forecast metric values of a metric of the object in a time interval from historical metric values of the metric. For each runtime metric value, a runtime residual value is computed based on the runtime metric value and a forecast metric value. Methods and system use spectral residual anomaly detection to determine in real time whether the runtime residual value indicates anomalous behavior of the object. In response to detecting anomalous behavior of the object, methods and system display an alert in a graphical user interface (“GUI”) of an electronic display device. The alert identifies the anomalous behavior of the object, the runtime metric value, and a time stamp of the anomalous behavior.