VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Abstract

Anomaly detection in time series data is a known problem, but recent growth in the number of units that can produce data require models that work on unlabelled and diverse types of data. We propose to adapt the neural network introduced by Simonyan and Zisserman in 2015 called VGG16 and used to detect and classify objects in images. We show that the VGG16 architecture with 2-dimensional convolutions replaced with 1-dimensional version could be a building block of an autoencoder approach to detect anomalies. Additionally we show that the proposed model achieves results that are similar or better than of classical anomaly detection methods.