Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node States
This study addresses the challenge of inferring unobserved node states in spatiotemporal prediction caused by insufficient sensor coverage. To this end, we propose GenST, a novel framework that pioneers the use of large language models (LLMs) as semantic bridges to compensate for missing signals. Specifically, GenST reformulates prediction as a conditional generation task: it extracts semantic features via LLMs and integrates a spatiotemporal variational autoencoder with a generative Transformer to reconstruct future states of unobserved nodes under multimodal conditioning. Extensive experiments demonstrate that GenST significantly outperforms existing baselines across multiple datasets, effectively mitigating data sparsity while achieving high-accuracy zero-shot prediction.