Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation

The architecture of the ADGCN model proposed in this paper is shown in Fig. 1 and consists of two main parts: (1) A unified spatiotemporal messaging layer constructed using attention mechanisms; (2) Dynamic graph convolution method using dynamic adjacency matrix in conjunction with static adjacency matrix, and gating to pass information. The detailed steps for each of these two sections are described separately below.

The architecture of the full model implementation is shown in Fig. 2, together with the vector dimensions of the model inputs and outputs….