  Robust Anomaly Detection (AD) on time series data is a key component for
monitoring many complex modern systems. These systems typically generate
high-dimensional time series that can be highly noisy, seasonal, and
inter-correlated. This paper explores some of the challenges in such data, and
proposes a new approach that makes inroads towards increased robustness on
seasonal and contaminated data, while providing a better root cause
identification of anomalies. In particular, we propose the use of Robust
Seasonal Multivariate Generative Adversarial Network (RSM-GAN) that extends
recent advancements in GAN with the adoption of convolutional-LSTM layers and
attention mechanisms to produce excellent performance on various settings. We
conduct extensive experiments in which not only do this model displays more
robust behavior on complex seasonality patterns, but also shows increased
resistance to training data contamination. We compare it with existing
classical and deep-learning AD models, and show that this architecture is
associated with the lowest false positive rate and improves precision by 30%
and 16% in real-world and synthetic data, respectively.
