Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders

Abstract

We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs employs a judiciously designed objective function based on robust divergences, including a, ß, and, -divergence, making it possible to separate anomalies from normal data without the reliance on anomaly labels, thus achieving robustness and fully unsupervised training. To better capture temporal dependencies in time series data, VQRAEs are built upon quasi-recurrent neural networks, which employ convolution and gating mechanisms to avoid the inefficient recursive computations used by classic recurrent neural networks. Further, VQRAEs can be extended to bi-directional Bi VQRAEs that utilize bi-directional information to further improve the accuracy. The above design choices make VQRAEs not only robust and thus accurate, but also efficient at detecting anomalies in streaming settings. Experiments on five real-world time series offer insight into the design properties of VQRAEs and demonstrate that VQRAEs are capable of outperforming state-of-the-art methods.