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.

翻译：视觉评估生成多元时间序列（MTS）的优度难以实现，特别是在生成模型为生成对抗网络（GANs）的情况下。我们提出一种名为高斯生成对抗网络（Gaussian GANs）的通用框架，用于在MTS生成任务中利用生成对抗网络自身进行视觉评估。首先，我们通过显式重构GANs架构，尝试寻找多元科尔莫戈罗夫-斯米尔诺夫检验（MKS检验）中的变换函数。其次，我们对变换后的MTS进行正态性检验，其中高斯GANs作为MKS检验中的变换函数。为简化正态性检验，我们提出一种基于卡方分布的高效可视化方法。在实验中，我们使用UniMiB数据集，提供实证证据表明基于高斯GANs和卡方可视化的正态性检验有效且可信。