Cross-city few-shot spatiotemporal graph forecasting via masked pre-training and prompt tuning

Spatiotemporal Graph (STG) forecasting holds great significance in the field of urban computing. However, the challenge of data scarcity poses significant obstacles to this task. While cross-city few-shot learning offers a promising solution, existing methods face two fundamental challenges: 1) insufficient extraction of meta-knowledge from data-rich source cities, and 2) limited generality of the knowledge transfer mechanism. In this paper, we propose a novel STG few-shot learning framework named ST-MPPT, which addresses both challenges through masked pre-training and prompt tuning. In the pre-training stage, we perform spatiotemporal-decoupled masked pre-training on source cities with abundant data, enabling the model to learn long-term spatiotemporal patterns more comprehensively. In the downstream forecasting stage, we leverage the pre-trained encoders to acquire robust spatial and temporal representations. These representations are then used to construct a graph structure and enhance the downstream spatiotemporal predictor. To achieve a more general knowledge transfer, we introduce a novel prompt network. Instead of rigid pattern retrieval, this network dynamically generates input-specific prompts to steer the pre-trained encoders to adapt to different data distributions across diverse cities. Extensive experiments on four real-world spatiotemporal datasets demonstrate the superiority of ST-MPPT over strong and representative baselines.