Over the last years, supervised learning has been a subject of great interest. However, in presence of unlabelled data, we face the problem of deep unsupervised learning. To overcome this issue in the context of anomaly detection in a cloud workload, we propose a method that relies on constraint programming (CP). After defining the notion of quasi-periodic extreme pattern in a time series, we propose an algorithm to acquire a CP model that is further used to annotate the cloud workload dataset. We finally propose a neural network model that learns from the annotated data to predict anomalies in a cloud workload. The relevance of the proposed method is shown by running simulations on real-world data traces and by comparing the accuracy of the predictions with those of a state of the art unsupervised learning algorithm.