Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applications, may be predicted with reasonable accuracy. We refer to them as predicted future covariates. However, existing methods that attempt to predict time series in an iterative manner with autoregressive models end up with exponential error accumulations. Other strategies hat consider the past and future in the encoder and decoder respectively limit themselves by dealing with the historical and future data separately. To address these limitations, a novel feature representation strategy -- shifting -- is proposed to fuse the past data and future covariates such that their interactions can be considered. To extract complex dynamics in time series, we develop a parallel deep learning framework composed of RNN and CNN, both of which are used hierarchically. We also utilize the skip connection technique to improve the model's performance. Extensive experiments on three datasets reveal the effectiveness of our method. Finally, we demonstrate the model interpretability using the Grad-CAM algorithm.

翻译：准确的时间序列预测是数据科学中的一项基本挑战。它常受到天气或人为干预等外部协变量的影响，而这些协变量在许多应用中可被合理准确地预测。我们将这些协变量称为预测的未来协变量。然而，现有方法尝试以自回归模型迭代预测时间序列，导致误差呈指数级积累。其他策略分别在编码器和解码器中考虑过去和未来数据，但因单独处理历史与未来数据而受限。为解决这些局限，本文提出一种新型特征表示策略——移位法——以融合历史数据与未来协变量，从而能够综合考虑它们之间的交互。为提取时间序列中的复杂动态，我们构建了一个由RNN和CNN组成的并行深度学习框架，两者均采用层次化结构。我们还利用跳连技术提升模型性能。在三个数据集上的广泛实验揭示了该方法的有效性。最后，我们通过Grad-CAM算法展示了模型的可解释性。