Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting

Weaver represents a significant advancement in spatiotemporal forecasting for transportation networks, addressing the complexities of traffic dynamics and social behaviors. Traditional Transformer-based models, while effective, often suffer from high computational demands and reduced interpretability. Weaver's innovative use of Kronecker product approximations allows for a more efficient decomposition of spatiotemporal attention, resulting in a model with reduced complexity of O(P^2N + N^2P). This efficiency is vital for real-world applications, where accurate and interpretable forecasting can enhance mobility and commerce. The introduction of Valence Attention, leveraging the continuous Tanimoto coefficient, further positions Weaver as a robust tool for traffic network analysis. As transportation networks become increasingly critical in modern society, the development of models like Weaver is essential for improving predictive capabilities and ensuring effective traffic management.