论文标题
如果您喜欢，请给它。概率的多元时间序列gan预测
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
论文作者
论文摘要
本文的贡献是两个方面。首先，我们提出探针 - 一种用于多元时间序列预测的新型概率模型。我们采用有条件的GAN框架来对我们的模型进行对抗训练。其次，我们提出了一个框架，使我们能够将确定性模型转换为具有改善性能的概率模型。该框架的动机是要么将现有高度准确的点预测模型转换为其概率对应物，要么通过仔细，高效地选择GAN组件的体系结构来稳定地训练gans。我们对两个公开可用数据集进行实验，即电力消耗数据集和Exchange-rate数据集。实验的结果证明了我们的模型的出色性能以及我们提出的框架的成功应用。
The contribution of this paper is two-fold. First, we present ProbCast - a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently. We conduct experiments over two publicly available datasets namely electricity consumption dataset and exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework.
