Superior Forecasting Performances with Multi-Graph Based Dynamic Learning Framework

The world of spatiotemporal data prediction has seen significant advancements with the introduction of our proposed multi-graph based dynamic learning framework. Through extensive experiments, we have demonstrated the remarkable forecasting capabilities of this framework on six real-world datasets from different cities and domains. The results not only validate the effectiveness of our approach but also showcase its impressive generalization ability.

To achieve these outstanding outcomes, we designed a novel Differential Vertex Representation (DVR) module that leverages differential information to model temporal trends. This innovative module is a significant departure from traditional methods, which often fail to capture dynamic correlations between vertices. By feeding both the Fixed Graph (FG) and Origin Graph (OG) into our proposed Multi-graph Fusion block, we are able to harness the strengths of each graph and create a more comprehensive representation.

One of the key limitations of existing spatiotemporal learning methods is their inability to handle frequently changing sequences. Our framework addresses this issue by introducing a novel Dynamic Neighbor Search (DNS) mechanism that constructs a feature graph with an adjacency matrix determined dynamically by DNS. This allows us to model global dynamic correlations between vertices more effectively than traditional methods.

In addition to its ability to capture dynamic trends, our framework also alleviates the over-smoothing issue often encountered in existing methods. By incorporating an Adaptive Heterogeneous Representation (AHR) module, we are able to adapt to changing patterns and relationships in the data, leading to more accurate predictions.

Our proposed multi-graph based dynamic learning framework is a significant improvement over traditional spatiotemporal data forecasting methods. These methods typically rely on aggregating features of local neighboring vertices in a fixed graph, which can only capture localized correlations between vertices. By using our framework, users can now tap into the full potential of their data and make more informed decisions.

In conclusion, our multi-graph based dynamic learning framework has shown remarkable forecasting performances on six real-world datasets from different cities and domains. Its ability to model temporal trends, alleviate over-smoothing issues, and capture global dynamic correlations makes it a valuable tool for spatiotemporal data prediction. With its superior generalization ability and effectiveness in handling frequently changing sequences, our framework is poised to revolutionize the field of graph data mining.

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