TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
It addresses a significant problem for applications like manufacturing and cybersecurity, but appears incremental as it adapts existing GAN methods to time series.
The paper tackles anomaly detection in time series data with limited data points, proposing TAnoGAN, a GAN-based unsupervised method, and shows it outperforms traditional and neural network models on 46 real-world datasets.
Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains. Extensive experimental results show that TAnoGan performs better than traditional and neural network models.