TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

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.

Made some minor changes. This is the accepted version of the paper at AusDM'20

References in corpus (3)

• Deep learning for time series classification: a review
• Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
• Anomaly Detection using Autoencoders in High Performance Computing Systems

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• Deep Learning for Time Series Anomaly Detection: A Survey
• A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
• Unsupervised Deep Learning for IoT Time Series
• Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach
• ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
• Evaluating GAN-LSTM for Smart Meter Anomaly Detection in Power Systems