Modeling complex spatial and temporal correlations in the correlated time
series data is indispensable for understanding the traffic dynamics and
predicting the future status of an evolving traffic system. Recent works focus
on designing complicated graph neural network architectures to capture shared
patterns with the help of pre-defined graphs. In this paper, we argue that
learning node-specific patterns is essential for traffic forecasting while the
pre-defined graph is avoidable. To this end, we propose two adaptive modules
for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a
Node Adaptive Parameter Learning (NAPL) module to capture node-specific
patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the
inter-dependencies among different traffic series automatically. We further
propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture
fine-grained spatial and temporal correlations in traffic series automatically
based on the two modules and recurrent networks. Our experiments on two
real-world traffic datasets show AGCRN outperforms state-of-the-art by a
significant margin without pre-defined graphs about spatial connections.

本文提出自适应图卷积循环神经网络 AGCRN，其中包括两个自适应模块（节点自适应参数学习模块和数据自适应图生成模块），用于捕捉交通时间序列数据中的空间和时间相关性。实验结果表明，AGCRN 在不需要预先定义空间连接图的情况下显著优于现有的相关方法。