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:
this https URL

本文提出了一种新的学习架构，该架构通过可学习的全连接硬图门控机制，实现了与最佳算法相竞争或更好的性能，进而在交通预测应用中实现了高效的时间序列预测。实验结果展示了该方法的价值，剖析研究证实了架构的每个元素的重要性。