Generative adversarial networks in time series: A survey and taxonomy

Abstract

Generative adversarial networks (GANs) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, making significant advancements. While these computer vision advances have garnered much attention, GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data. In this paper, we review GAN variants designed for time series related applications. We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Here we showcase the latest and most popular literature in this field; their architectures, results, and applications. We also provide a list of the most popular evaluation metrics and their suitability across applications. Also presented is a discussion of privacy measures for these GANs and further protections and directions for dealing with sensitive data. We aim to frame clearly and concisely the latest and state-of-the-art research in this area and their applications to real-world technologies.

References in corpus (9)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
• Synthetic Medical Images from Dual Generative Adversarial Networks
• Health Data in an Open World
• FedGAN: Federated Generative Adversarial Networks for Distributed Data
• Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

Cited by in corpus (1)

• ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables