论文标题
FC-GAGA：空间交通预测的完全连接的封闭式图形体系结构
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
论文作者
论文摘要
对多元时间序列的预测是一个重要的问题，它在流量管理，蜂窝网络配置和定量融资中具有应用。当有一个可用的图表捕获时间序列之间的关系时，就会出现问题的特殊情况。在本文中，我们提出了一种新颖的学习体系结构，该架构与最佳现有算法相比，在不需要图形的情况下，具有比最好的现有算法具有竞争力。我们提出的体系结构的关键要素是可学习的完全连接的硬图门控机制，该机制能够使用最先进的和高度计算的完全连接的完全连接的时间序列预测架构在流量预测应用程序中。两个公共交通网络数据集的实验结果说明了我们方法的价值，消融研究证实了架构的每个元素的重要性。该代码可在此处提供：https：//github.com/boreshkinai/fc-gaga。
Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this paper we propose a novel learning architecture that achieves performance competitive with or better than the best existing algorithms, without requiring knowledge of the graph. The key element of our proposed architecture is the learnable fully connected hard graph gating mechanism that enables the use of the state-of-the-art and highly computationally efficient fully connected time-series forecasting architecture in traffic forecasting applications. Experimental results for two public traffic network datasets illustrate the value of our approach, and ablation studies confirm the importance of each element of the architecture. The code is available here: https://github.com/boreshkinai/fc-gaga.
