Air quality prediction is a typical spatio-temporal modeling problem, which
always uses different components to handle spatial and temporal dependencies in
complex systems separately. Previous models based on time series analysis and
Recurrent Neural Network (RNN) methods have only modeled time series while
ignoring spatial information. Previous GCNs-based methods usually require
providing spatial correlation graph structure of observation sites in advance.
The correlations among these sites and their strengths are usually calculated
using prior information. However, due to the limitations of human cognition,
limited prior information cannot reflect the real station-related structure or
bring more effective information for accurate prediction. To this end, we
propose a novel Dynamic Graph Neural Network with Adaptive Edge Attributes
(DGN-AEA) on the message passing network, which generates the adaptive
bidirected dynamic graph by learning the edge attributes as model parameters.
Unlike prior information to establish edges, our method can obtain adaptive
edge information through end-to-end training without any prior information.
Thus reduced the complexity of the problem. Besides, the hidden structural
information between the stations can be obtained as model by-products, which
can help make some subsequent decision-making analyses. Experimental results
show that our model received state-of-the-art performance than other baselines.

本研究提出了一种用于空气质量预测的动态图神经网络，使用自适应边属性进行边信息传递，并通过端到端训练获取自适应边信息，从而提高模型表现。