Causality-Inspired Spatio-Temporal Attention for Forecasting on Evolving Graphs

Urban forecasting is particularly critical when infrastructure changes, as road-network and communication entities may be added, removed, or disrupted, altering graph topology and data distributions. Most spatio-temporal forecasters assume fixed node sets and connectivity. Newly activated entities lack sufficient long-term observations, while frequent retraining may be impractical for time-sensitive traffic control and emergency recovery. We therefore study multi-step forecasting on evolving graphs and propose DyCSTA, a spatio-temporal forecasting framework for multi-step prediction on evolving graphs. DyCSTA retains usable history for persistent nodes, models newly arriving nodes from their current observations, reconstructs dependencies on the current graph, and uses latent context to assist prediction. Its predictive representation split separates forecast-relevant dynamics from context-related variation, while a model-defined replacement branch regularizes context conditioning during training. Experiments on traffic, communication, and other evolving systems evaluate changing node sets, new-node forecasting, and all prediction horizons. DyCSTA achieves its strongest results on the two flow datasets and remains competitive in the other domains.