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Seyfi, Ali

University of British Columbia

Multivariate time series generation is a promising method for sharing sensitive data in numerous medical, financial, and Internet of Things applications. A common type of multivariate time series is derived from a single source, such as the biometric measurements of a patient. Originating from a single source results in intricate dynamical patterns between individual time series that are difficult for typical generative models such as Generative Adversarial Network (GAN)s to learn. Machine learning models can use the valuable information in those patterns to better classify, predict, or perform other downstream tasks. GroupGAN is a novel framework that considers time series’ common origin and favors preserving inter-channel relationships. The two critical points of the GroupGAN method are: 1) the individual time series are generated from a common point in latent space, and 2) a central discriminator favors the preservation of inter-channel dynamics. We demonstrate empirically that the GroupGAN method helps preserve channel correlations and that the synthetic data generated using the GroupGAN method performs very well downstream tasks with medical and financial data.

Text

2022-07-26

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82199

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/