A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics
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In this paper we propose a novel machine-learning method for anomaly detection. Focusing on 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 nets is implemented for a 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.
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