A Review of Spatiotemporal GANs for Telemetry Data Anomaly Detection

Abstract

Telemetry data anomaly detection is a crucial task in various domains, including aerospace, power systems, and environmental monitoring. In recent years, significant advancements have been made in the development of anomaly detection techniques, particularly with the advent of spatial-temporal generative adversarial networks (ST-GANs). This review paper aims to provide a comprehensive overview of the progress in telemetry data anomaly detection, with a specific focus on the application of ST-GANs. The review begins by emphasizing the importance of telemetry data anomaly detection and highlighting the challenges associated with traditional methods. Subsequently, it delves into the underlying principles of ST-GANs and their suitability for detecting anomalies in complex, time-series data. The paper presents a detailed analysis of experimental results and performance comparisons of ST-GANs with other state-of-the-art anomaly detection algorithms, such as LSTM-GAN, Isolation Forest, and GRU-VAE.