TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Md Abul Bashar, Richi Nayak
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.
Topics & Concepts
Anomaly detectionComputer scienceSeries (stratigraphy)Generative grammarAdversarial systemArtificial intelligenceTime seriesVariety (cybernetics)Anomaly (physics)Pattern recognition (psychology)Artificial neural networkCover (algebra)Machine learningData miningImage (mathematics)Deep learningData pointGenerative modelUnsupervised learningGenerative adversarial networkFeature extractionTraining setSynthetic dataFeature (linguistics)Anomaly Detection Techniques and ApplicationsDigital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis