TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

• URL: http://arxiv.org/abs/2008.09567v2
• Date: Fri, 25 Sep 2020 01:50:33 GMT
• Title: TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
• Authors: Md Abul Bashar, Richi Nayak
• Abstract summary: We propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains.
• Score: 1.9290392443571387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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