Studies on the GAN-Based Anomaly Detection Methods for the Time Series Data

Abstract

Anomaly detection (AD) for times series data using the generative adversarial network (GAN) has been proposed in recent years. According to the previous study, the GAN-based AD outperformed the cumulative sum (CUSUM) chart. However, no framework for comparison is provided in their works. So, we conduct new studies crucial for the GAN-based AD methods (the MAD-GAN and the TAnoGAN). First, we propose a new framework for fair and systematic comparisons for the prediction performance of the GAN-based AD methods as well as the cumulative sum (CUSUM) chart. So, we evaluate the three methods with four simulation data and secure water treatment system data. Under the proposed comparison framework, the CUSUM chart generally shows prediction performances better than the GAN-based AD methods. Our results imply that more follow-up studies are required before deploying the GAN-based AD methods. Second, we find that adjusting the number of backpropagation steps of the inverse mapping technique can improve the prediction performance of the GAN-based AD methods. Furthermore, we find that monitoring the residuals of the fitted model significantly improves the prediction performance of the GAN-based AD methods as well as the CUSUM chart.

Keywords

• Anomaly detection
• time series data
• comparison framework
• generative adversarial network
• cumulative sum chart
• backpropagation