论文标题
Wasserstein Gans的统计分析，应用时间序列预测
Statistical analysis of Wasserstein GANs with applications to time series forecasting
论文作者
论文摘要
我们在依赖观测的框架中为条件和无条件的Wasserstein生成对抗网络（WGAN）提供统计理论。我们证明，相对于修改的瓦斯坦型距离，过量的贝叶斯风险过多的贝叶斯风险。此外，我们对估计量较弱的收敛性进行形式化和得出陈述，并使用它们来为新观察开发置信区间。该理论应用于高维时间序列预测的特殊情况。我们根据综合数据分析估计量的行为，并使用温度数据研究一个真实的数据示例。数据的依赖性通过绝对常规的混合β系数量化。
We provide statistical theory for conditional and unconditional Wasserstein generative adversarial networks (WGANs) in the framework of dependent observations. We prove upper bounds for the excess Bayes risk of the WGAN estimators with respect to a modified Wasserstein-type distance. Furthermore, we formalize and derive statements on the weak convergence of the estimators and use them to develop confidence intervals for new observations. The theory is applied to the special case of high-dimensional time series forecasting. We analyze the behavior of the estimators in simulations based on synthetic data and investigate a real data example with temperature data. The dependency of the data is quantified with absolutely regular beta-mixing coefficients.
