  In this paper we propose a novel machine-learning method for anomaly
detection applicable to data with periodic characteristics where randomly
varying period lengths are explicitly allowed. A multi-dimensional time series
analysis is conducted by training a data-adapted classifier consisting of deep
convolutional neural networks performing phase classification. The entire
algorithm including data pre-processing, period detection, segmentation, and
even dynamic adjustment of the neural networks is implemented for fully
automatic execution. The proposed method is evaluated on three example datasets
from the areas of cardiology, intrusion detection, and signal processing,
presenting reasonable performance.
