From Temporal Priors to Spatial Reasoning: Graph Adaptation of Frozen Time-Series Foundation Models

Recent spatio-temporal foundation models (STFMs) have demonstrated strong transferability and forecasting capabilities in zero-shot and few-shot settings, but they usually require large-scale spatio-temporal pre-training and substantial computational resources. Moreover, spatial dependencies are often highly domain-specific, making it difficult for a pre-trained spatio-temporal backbone to generalize across heterogeneous spatial structures. To address these limitations, we propose Graft, a framework that integrates lightweight graph adaptation into frozen time-series foundation models (TSFMs), extending them to downstream spatio-temporal forecasting without modifying the foundation backbone. Specifically, Graft extracts latent forecast tokens from the frozen TSFM while constructing temporally aligned relational graph tokens from historical observations, node attributes, and target-domain graph structures. The graph tokens are projected into the forecast-token space and adaptively integrated with the forecast tokens through gated temporal-graph fusion. We further develop a causal-mediated graph adapter (CMGA) that models hierarchical graph dependencies and dynamically injects graph-structured corrections into the temporal representations. Extensive experiments on seven spatio-temporal forecasting benchmarks demonstrate that the proposed method achieves strong few-shot performance and favorable efficiency compared with full TSFM fine-tuning. Further experiments on multivariate time-series anomaly detection validate its generalization across different graph semantics.