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

翻译：尽管大语言模型（LLMs）在自然语言处理和计算机视觉等任务中占据主导地位，但如何利用其能力进行时空预测仍具挑战性。顺序文本与复杂时空数据之间的差异阻碍了这一应用。为解决此问题，本文提出STG-LLM——一种赋能LLMs进行时空预测的创新方法。我们通过以下设计应对数据不匹配问题：1）STG-Tokenizer：该时空图分词器将复杂图数据转换为捕捉空间与时间关系的紧凑标记；2）STG-Adapter：该极简适配器由线性编码与解码层组成，弥合了标记化数据与LLM理解之间的鸿沟。通过仅微调少量参数，它既能有效捕捉STG-Tokenizer生成的标记语义，又能保留LLM原有的自然语言理解能力。在多样化的时空基准数据集上的大量实验表明，STG-LLM成功释放了LLM在时空预测中的潜力。值得注意的是，我们的方法取得了与专用SOTA方法相媲美的竞争性性能。