.png){kind=icon}
Return
Heuristic Knowledge-Driven Spatio-Temporal Forecasting via Multigraph
.png){kind=icon}
PRE
AI
DOI：10.1109/tnnls.2026.3656372.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The importance of precise long-term forecasting in practical applications continues to rise. Extensive scenarios, including parking resource prediction and environmental quality monitoring, rely significantly on LSTF’s accurate spatio-temporal forecasting capabilities. This technology strengthens prediction effectiveness by combining interaction relationships between spatial-temporal dimensions with contextual data integration. Over time, graph neural networks (GNNs) have proven highly effective in capturing spatial interdependencies. Recent advances have introduced multi-GNNs (MGNNs), which incorporate more contextual insights to improve predictive accuracy. However, when MGNNs are applied to long-term spatio-temporal forecasting (LSTF), they encounter challenges such as limited generality, under-utilization of context, static graph merging methods, and overlooking dynamic interrelations. To address these issues, we propose novel graph structures that encode each node’s contextual information while fully exploiting long-term spatio-temporal dependencies. Furthermore, this research designs a dynamic multigraph fusion architecture that integrates spatial dimensions, temporal features, and graph attention mechanisms to simultaneously capture intragraph node correlations and cross-graph interactions. To strengthen relational analysis, trainable weight tensors are employed for quantitative evaluation of node importance across graphs. Systematic experiments on three large-scale benchmark datasets confirm that this approach achieves significant performance enhancement for existing GNNs in LSTF tasks.
Keywords:
Attention mechanisms
deep learning
graph neural networks (GNNs)
long-term spatio-temporal forecasting (LSTF)
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction.png)
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
OAAI
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAIShareSave
ShareSaveShareSaveShareSaveShareSave
View more