  Robust anomaly detection is a requirement for monitoring complex modern
systems with applications such as cyber-security, fraud prevention, and
maintenance. These systems generate multiple correlated time series that are
highly seasonal and noisy. This paper presents a novel unsupervised deep
learning architecture for multivariate time series anomaly detection, called
Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN). It
extends recent advancements in GANs with adoption of convolutional-LSTM layers
and an attention mechanism to produce state-of-the-art performance. We conduct
extensive experiments to demonstrate the strength of our architecture in
adjusting for complex seasonality patterns and handling severe levels of
training data contamination. We also propose a novel anomaly score assignment
and causal inference framework. We compare RSM-GAN with existing classical and
deep-learning based anomaly detection models, and the results show that our
architecture is associated with the lowest false positive rate and improves
precision by 30% and 16% in real-world and synthetic data, respectively.
Furthermore, we report the superiority of RSM-GAN regarding accurate root cause
identification and NAB scores in all data settings.
