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In this paper, Lia Ahrens, Julian Ahrens and Hans D. Schotten proposes a novel approach to detecting anomalies in time series exhibiting periodic characteristics, where we applied deep convolutional neural networks for phase classification and automated phase similarity tagging. They evaluated their approach on three example datasets corresponding to the domains of cardiology, industry, and signal processing, confirming that their method is feasible in a number of contexts.