论文标题
Tanogan：使用生成对抗网络的时间序列异常检测
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
论文作者
论文摘要
时间序列数据中的异常检测是许多应用领域（例如制造，医学成像和网络安全）所面临的重大问题。最近，生成的对抗网络（GAN）引起了图像域中的发电和异常检测的注意。在本文中，我们提出了一种新型的基于GAN的无监督方法，称为Tanogan，用于检测少数数据点的时间序列中的异常。我们使用涵盖各种域的46个现实世界中时间序列数据集评估塔诺根。广泛的实验结果表明，Tanogan的性能比传统和神经网络模型更好。
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.
