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：尽管大型语言模型（LLMs）在自然语言处理和计算机视觉等任务中占据主导地位，但将其能力用于时空预测仍然充满挑战。顺序文本与复杂时空数据之间的差异阻碍了这一应用。为解决这一问题，本文提出了STG-LLM，一种创新的方法，旨在赋能LLMs进行时空预测。我们通过以下方式解决数据不匹配问题：1）STG-Tokenizer：这种时空图分词器将复杂的图数据转化为简洁的标记，同时捕捉空间和时间关系；2）STG-Adapter：这种极简的适配器，由线性编码和解码层组成，弥合了分词数据与LLM理解之间的差距。通过仅微调少量参数，它能有效理解STG-Tokenizer生成的标记语义，同时保留LLMs原有的自然语言理解能力。在多种时空基准数据集上的广泛实验表明，STG-LLM成功释放了LLMs在时空预测方面的潜力。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能。