Generative Adversarial Networks in Time Series: A Systematic Literature Review
Dublin City University · Trinity College Dublin
Abstract
Generative adversarial network (GAN) 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. Although 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 article, we review GAN variants designed for time series related applications. We propose a classification of discrete-variant GANs and continuous-variant GANs, in…
Citation impact
- FWCI
- 27.30
- Percentile
- 100%
- References
- 73
Authors
4Topics & keywords
- Computer science
- Adversarial system
- Field (mathematics)
- Generative grammar
- Data science
- Series (stratigraphy)
- Generative adversarial network
- Time series