A Critical Analysis of Generative Adversarial Networks in Anomaly Detection for Time Series

Anomaly detection has applications across different knowledge domains and is intricately linked to numerous problems, such as fault detection for industrial and measurement systems. However, the usual completely unsupervised nature of the problem complicates and restricts the application of various intelligent models. In this context, solutions based on GANs for modelling distributions and arbitrary processes with unsupervised data show potential in anomaly detection. This work addresses a solution based on the TadGAN architecture in the unsupervised detection of anomalies in time series. Initially, a brief review of the state of the art on essential concepts about anomalies in time series is provided, as well as the main works involving GANs in this respective area. Subsequently, the TadGAN architecture is assessed utilising the proposed methodology, wherein its principles and primary limitations are discussed, such as the absence of standardisation in performance evaluation metrics. As an innovation, we assess TadGAN using experimental data and propose new metrics to quantify the anomalous state from both the model and the data. The obtained results confirm the significant potential of GANs in detecting anomalies in time series.