Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models on real- and simulated data, our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.

翻译：在此,我们提出一种一般的概率时间序列预测方法。 我们将自动递减的经常性神经网络与隐含量子网络结合起来,以模拟时间动态,以学习时间序列目标的大量分布。 与其他真实数据和模拟数据概率神经预测模型相比,我们的方法在点向预测准确性和估计基本时间分布方面是有利的。