Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection

TL;DR

This paper introduces RSM-GAN, a novel generative adversarial network architecture designed to improve robustness and accuracy in anomaly detection for complex, seasonal, high-dimensional time series data, outperforming existing models.

Contribution

The paper proposes RSM-GAN, a new GAN-based approach with convolutional-LSTM and attention mechanisms, enhancing robustness against seasonality and contamination in multivariate time series anomaly detection.

Findings

RSM-GAN shows superior robustness on seasonal data.

The model achieves a 30% increase in precision on real-world data.

It maintains low false positive rates compared to classical methods.

Abstract

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.