Echo state conditional variational autoencoder for anomaly detection
Anomaly detection involves identifying the events which do not conform to an expected pattern in data. A common approach to anomaly detection is to identify outliers in a latent space learned from data. For instance, PCA has been successfully used for anomaly detection. Variational autoencoder (VAE) is a recently developed deep generative model which has established itself as a powerful method for learning representation from data in a nonlinear way. However, the VAE does not take the temporal dependence in data into account, so it limits its applicability to time series. In this paper we combine the echo state network, which is a simple training method for recurrent networks, with the VAE, in order to learn representation from multivariate time series data. We present an echo state conditional variational autoencoder (ES CVAE) and demonstrate its useful behavior in the task of anomaly detection in multivariate time series data.
2016 International Joint Conference on Neural Networks (IJCNN)