论文标题
混合密度有条件生成对抗网络模型（MD-CGAN）
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
论文作者
论文摘要
近年来，生成的对抗网络（GAN）引起了人们的重大关注，特别是在计算机视觉中强调的应用程序。然而，与此类示例相比，甘斯对时间序列建模的应用有限，包括预测。在这项工作中，我们介绍了混合密度条件生成对抗模型（MD-CGAN），重点是时间序列预测。我们表明，我们的模型能够估算预测的概率后验分布，并且与一组基准方法相比，MD-CGAN模型的性能很好，尤其是在噪声是观察到时间序列的重要组成部分的情况下。此外，通过使用高斯混合模型作为输出分布，MD-CGAN提供了非高斯的后验预测。
Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modelling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the MD-CGAN model performs well, particularly in situations where noise is a significant component of the observed time series. Further, by using a Gaussian mixture model as the output distribution, MD-CGAN offers posterior predictions that are non-Gaussian.
