  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.
