1. TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Md Abul Bashar, Richi Nayak
Anomaly detection in time series data is a significant problem faced in many application areas. 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.
TAnoGAN is a method that uses GANs for time series anomaly detection. https://t.co/pDgNNeuxfF pic.twitter.com/rULMy9iO0R
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