  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.
