论文标题
时间序列的深度学习预测：教程和文学调查
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
论文作者
论文摘要
在时间序列预测或预测的许多应用中，基于预测方法的深度学习已成为选择的选择方法，通常通常优于其他方法。因此，在过去的几年中，这些方法现在在大规模的工业预测应用中无处不在，并且一直在预测竞赛（例如M4和M5）中始终排名。这种实践上的成功进一步提高了学术兴趣，以理解和改善深厚的预测方法。在本文中，我们提供了该领域的介绍和概述：我们为深度预测的重要构件提出了一定深度的深度构建块；然后，我们使用这些构建块，然后调查最近深层预测文献的广度。
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.
