Anomaly detection with Wasserstein GAN
This work addresses anomaly detection for time series data, but it appears incremental as it builds on existing GAN methods with modifications.
The paper tackles anomaly detection in time series by using a Wasserstein GAN with a stacked encoder to learn normal data distributions, achieving state-of-the-art scores on the MNIST dataset and exploring its application to multivariate time series.
Generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. In this paper, we investigate GAN to perform anomaly detection on time series dataset. In order to achieve this goal, a bibliography is made focusing on theoretical properties of GAN and GAN used for anomaly detection. A Wasserstein GAN has been chosen to learn the representation of normal data distribution and a stacked encoder with the generator performs the anomaly detection. W-GAN with encoder seems to produce state of the art anomaly detection scores on MNIST dataset and we investigate its usage on multi-variate time series.