Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series

arXiv:2208.02649v2 Announce Type: replace
Abstract: Visually evaluating the goodness of generated Multivariate Time Series (MTS) are difficult to implement, especially in the case that the generative model is Generative Adversarial Networks (GANs). We present a general framework named Gaussian GANs to visually evaluate GANs using itself under the MTS generation task. Firstly, we attempt to find the transformation function in the multivariate Kolmogorov Smirnov (MKS) test by explicitly reconstructing the architecture of GANs. Secondly, we conduct the normality test of …

abstract adversarial arxiv case cs.lg eess.sp framework gans general generated generative generative adversarial networks multivariate networks series stat.ap time series type