Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction

Understand

Training urban spatio-temporal foundation models that generalize well across diverse regions and cities is critical for deploying urban services in unseen or data-scarce regions.

• Recent studies have typically focused on fusing cross-domain spatio-temporal data to train unified Transformer-based models.
• However, these models suffer from quadratic computational complexity and high memory overhead, limiting their scalability and practical deployment.
• Inspired by the efficiency of Mamba, a state space model with linear time complexity, we explore its potential for efficient urban spatio-temporal prediction.