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In this paper we propose a novel machine-learning method for anomalydetection applicable to data with periodic characteristics where randomlyvarying period lengths are explicitly allowed. A multi-dimensional time seriesanalysis is conducted by training a data-adapted classifier consisting of deepconvolutional neural networks performing phase classification. The entirealgorithm including data pre-processing, period detection, segmentation, andeven dynamic adjustment of the neural networks is implemented for fullyautomatic execution. The proposed method is evaluated on three example datasetsfrom the areas of cardiology, intrusion detection, and signal processing,presenting reasonable performance.

A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics