Spatial-temporal forecasting and imputation are important for real-world dynamic systems such as intelligent transportation, urban planning, and public health. Most existing methods are tailored for individual forecasting or imputation tasks but are not designed for both. Additionally, they are less effective for zero-shot and few-shot learning. While large language models (LLMs) have exhibited strong pattern recognition and reasoning abilities across various tasks, including few-shot and zero-shot learning, their development in understanding spatial-temporal data has been constrained by insufficient modeling of complex correlations such as the temporal correlations, spatial connectivity, non-pairwise and high-order spatial-temporal correlations within data. In this paper, we propose STD-LLM for understanding both spatial and temporal properties of \underline{S}patial-\underline{T}emporal \underline{D}ata with \underline{LLM}s, which is capable of implementing both spatial-temporal forecasting and imputation tasks. STD-LLM understands spatial-temporal correlations via explicitly designed spatial and temporal tokenizers as well as virtual nodes. Topology-aware node embeddings are designed for LLMs to comprehend and exploit the topology structure of data. Additionally, to capture the non-pairwise and higher-order correlations, we design a hypergraph learning module for LLMs, which can enhance the overall performance and improve efficiency. Extensive experiments demonstrate that STD-LLM exhibits strong performance and generalization capabilities across the forecasting and imputation tasks on various datasets. Moreover, STD-LLM achieves promising results on both few-shot and zero-shot learning tasks.

翻译：时空预测与插补对于智能交通、城市规划、公共卫生等现实世界动态系统至关重要。现有方法大多专为单一预测或插补任务设计，难以兼顾两者，且在零样本与少样本学习场景下效果有限。尽管大语言模型（LLMs）已在包括少样本与零样本学习在内的多种任务中展现出强大的模式识别与推理能力，但其在理解时空数据方面的发展受限于对数据内部复杂关联（如时间相关性、空间连通性、非成对及高阶时空相关性）建模的不足。本文提出STD-LLM，旨在利用大语言模型理解时空数据的空间与时间特性，该模型能够同时实现时空预测与插补任务。STD-LLM通过显式设计的空间与时间分词器以及虚拟节点来理解时空关联。模型设计了拓扑感知的节点嵌入，使大语言模型能够理解并利用数据的拓扑结构。此外，为捕捉非成对及高阶相关性，我们为大语言模型设计了超图学习模块，该模块可提升整体性能并提高效率。大量实验表明，STD-LLM在多个数据集的预测与插补任务中均展现出卓越的性能与泛化能力。同时，该模型在少样本与零样本学习任务上也取得了显著成果。