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

arXiv: https://arxiv.org/abs/1811.12119 · HTML: https://mlsanity.com/paper/1811.12119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder](https://mlsanity.com/paper/2304.03420/) (2023)

## Abstract (arXiv)

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.