Adaptable graph region for optimizing performance in dynamic system long-term forecasting via time-aware expert

Area of Science:

• Computational Science
• Artificial Intelligence
• Graph Neural Networks

Background:

• Accurate long-term prediction of dynamic systems is vital for risk identification.
• Current neural network models prioritize accuracy over computational efficiency.
• System scale significantly impacts computational efficiency in predictions.

Purpose of the Study:

• To develop a prediction method that optimizes both accuracy and computational efficiency.
• To address the limitations of existing models in handling large-scale dynamic systems.
• To enable practical real-time forecasting for dynamic systems.

Main Methods:

• Proposed regional graph representation to reduce graph structure scale by merging nodes into regions.
• Utilized graph convolution or lightweight convolution modules for topological information extraction.
• Introduced a sparse time-aware expert module for dynamic multi-scale temporal information modeling.

Main Results:

• Achieved an optimal balance between prediction speed and accuracy.
• Demonstrated adaptability of the regional graph representation to all graph-based models.
• Provided a practical solution for real-time forecasting challenges.

Conclusions:

• The proposed architecture offers a significant improvement in computational efficiency for dynamic system prediction.
• This method enables effective multi-scale modeling of temporal information.
• The approach provides a viable solution for real-time risk identification in dynamic systems.