GATGPT：一种结合图注意力网络与预训练大型语言模型的时空插补方法
GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation
AI总结：
本研究提出GATGPT框架，将图注意力机制与预训练大型语言模型结合用于时空插补，通过保持大部分参数不变并微调上层，在三个真实数据集上取得与深度学习基准相当的效果。
摘要：
时空数据分析在交通、医疗和气象等多个领域的应用日益增多。在现实环境中，由于传感器故障和数据传输错误等问题，此类数据通常包含缺失元素。时空插补的目标是通过理解观测到的多元时间序列中固有的空间和时间关系来估计这些缺失值。传统上，时空插补依赖于为此目的设计的特定复杂架构，这些架构存在适用性有限和高计算复杂度的问题。相比之下，我们的方法将预训练大型语言模型（LLMs）引入时空插补，提出了一个开创性框架GATGPT。该框架将图注意力机制与LLMs相结合。我们保持大部分LLM参数不变，以利用已有知识学习时间模式，同时针对不同应用微调上层结构。图注意力组件增强了LLM理解空间关系的能力。通过在三个不同的真实世界数据集上的测试，我们的创新方法取得了与现有深度学习基准相当的结果。
英文摘要：
The analysis of spatiotemporal data is increasingly utilized across diverse domains, including transportation, healthcare, and meteorology. In real-world settings, such data often contain missing elements due to issues like sensor malfunctions and data transmission errors. The objective of spatiotemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed multivariate time series. Traditionally, spatiotemporal imputation has relied on specific, intricate architectures designed for this purpose, which suffer from limited applicability and high computational complexity. In contrast, our approach integrates pre-trained large language models (LLMs) into spatiotemporal imputation, introducing a groundbreaking framework, GATGPT. This framework merges a graph attention mechanism with LLMs. We maintain most of the LLM parameters unchanged to leverage existing knowledge for learning temporal patterns, while fine-tuning the upper layers tailored to various applications. The graph attention component enhances the LLM's ability to understand spatial relationships. Through tests on three distinct real-world datasets, our innovative approach demonstrates comparable results to established deep learning benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
