  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 transformed
MST where the Gaussian GANs serves as the transformation function in the MKS
test. In order to simplify the normality test, an efficient visualization is
proposed using the chi square distribution. In the experiment, we use the
UniMiB dataset and provide empirical evidence showing that the normality test
using Gaussian GANs and chi sqaure visualization is effective and credible.
