  Many real-world ubiquitous applications, such as parking recommendations and
air pollution monitoring, benefit significantly from accurate long-term
spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency
between spatial and temporal domains, contextual information, and inherent
pattern in the data. Recent studies have revealed the potential of multi-graph
neural networks (MGNNs) to improve prediction performance. However, existing
MGNN methods cannot be directly applied to LSTF due to several issues: the low
level of generality, insufficient use of contextual information, and the
imbalanced graph fusion approach. To address these issues, we construct new
graph models to represent the contextual information of each node and the
long-term spatio-temporal data dependency structure. To fuse the information
across multiple graphs, we propose a new dynamic multi-graph fusion module to
characterize the correlations of nodes within a graph and the nodes across
graphs via the spatial attention and graph attention mechanisms. Furthermore,
we introduce a trainable weight tensor to indicate the importance of each node
in different graphs. Extensive experiments on two large-scale datasets
demonstrate that our proposed approaches significantly improve the performance
of existing graph neural network models in LSTF prediction tasks.
