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。它常常受到外部协变量（如天气或人为干预）的影响，而在许多应用中，这些协变量可以被合理准确地预测。我们将这些变量称为预测的未来协变量。然而，现有方法尝试通过自回归模型以迭代方式预测时间序列，最终导致指数级误差累积。其他策略分别在编码器和解码器中考虑过去和未来数据，从而限制了自身，因为它们单独处理历史数据和未来数据。为解决这些局限性，我们提出了一种新的特征表示策略——移位（shifting），用于融合过去数据和未来协变量，从而能够考虑它们之间的相互作用。为提取时间序列中的复杂动态，我们开发了一个由RNN和CNN组成的并行深度学习框架，两者均以层级方式使用。我们还利用跳跃连接技术来提升模型性能。在三个数据集上的大量实验揭示了我们方法的有效性。最后，我们使用Grad-CAM算法展示了模型的可解释性。