Heuristic Knowledge-Driven Spatio-Temporal Forecasting via Multigraph.

Area of Science:

• Artificial Intelligence
• Data Science
• Machine Learning

Background:

• Long-term spatio-temporal forecasting (LSTF) is crucial for applications like resource prediction and environmental monitoring.
• Graph neural networks (GNNs) excel at capturing spatial dependencies, with multi-GNNs (MGNNs) offering enhanced contextual insights.
• Existing MGNNs face challenges in LSTF, including limited generality, under-utilization of context, static graph merging, and overlooking dynamic interrelations.

Purpose of the Study:

• To develop novel graph structures and a dynamic multigraph fusion architecture for improved LSTF.
• To address the limitations of current MGNNs in capturing dynamic interrelations and contextual information.
• To enhance the accuracy and effectiveness of LSTF through advanced GNN techniques.

Main Methods:

• Proposed novel graph structures encoding node contextual information and exploiting long-term spatio-temporal dependencies.
• Designed a dynamic multigraph fusion architecture integrating spatial, temporal, and graph attention mechanisms.
• Employed trainable weight tensors for quantitative evaluation of node importance across graphs.

Main Results:

• The proposed approach significantly enhances the performance of existing GNNs in LSTF tasks.
• Demonstrated improved accuracy in capturing intragraph node correlations and cross-graph interactions.
• Validated through systematic experiments on three large-scale benchmark datasets.

Conclusions:

• The novel graph structures and dynamic fusion architecture effectively address limitations in current LSTF methods.
• This research provides a robust framework for improving the accuracy of long-term spatio-temporal forecasting.
• The findings offer significant advancements for GNN applications in complex forecasting scenarios.