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Conditional GAN for timeseries generation
[article]
2020
arXiv
pre-print
It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an
arXiv:2006.16477v1
fatcat:mqiyx4dgdrftrh3eu5wy6cuayq