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While Large Language Models (LLMs) dominate tasks like natural languageprocessing and computer vision, harnessing their power for spatial-temporalforecasting remains challenging. The disparity between sequential text andcomplex spatial-temporal data hinders this application. To address this issue,this paper introduces STG-LLM, an innovative approach empowering LLMs forspatial-temporal forecasting. We tackle the data mismatch by proposing: 1)STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graphdata into concise tokens capturing both spatial and temporal relationships; 2)STG-Adapter: This minimalistic adapter, consisting of linear encoding anddecoding layers, bridges the gap between tokenized data and LLM comprehension.By fine-tuning only a small set of parameters, it can effectively grasp thesemantics of tokens generated by STG-Tokenizer, while preserving the originalnatural language understanding capabilities of LLMs. Extensive experiments ondiverse spatial-temporal benchmark datasets show that STG-LLM successfullyunlocks LLM potential for spatial-temporal forecasting. Remarkably, ourapproach achieves competitive performance on par with dedicated SOTA methods.

How Can Large Language Models Understand Spatial-Temporal Data?