# GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation

arXiv: https://arxiv.org/abs/2311.14332 · HTML: https://mlsanity.com/paper/2311.14332/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [GP-VAE: Deep Probabilistic Time Series Imputation](https://mlsanity.com/paper/1907.04155/) (2019)
- [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022)
- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://mlsanity.com/paper/2308.08241/) (2023)
- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://mlsanity.com/paper/2308.08469/) (2023)
- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2310.08278/) (2023)
- [Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language](https://mlsanity.com/paper/2306.16410/) (2023)

## Built on by (most cited first, 30 of 35)

- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [Large Language Models for Time Series: A Survey](https://mlsanity.com/paper/2402.01801/) (2024)
- [Spatial-Temporal Large Language Model for Traffic Prediction](https://mlsanity.com/paper/2401.10134/) (2024)
- [Empowering Time Series Analysis with Large Language Models: A Survey](https://mlsanity.com/paper/2402.03182/) (2024)
- [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://mlsanity.com/paper/2402.02713/) (2024)
- [LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law](https://mlsanity.com/paper/2402.00795/) (2024)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models](https://mlsanity.com/paper/2403.02221/) (2024)
- [Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2405.02358/) (2024)
- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://mlsanity.com/paper/2405.11333/) (2024)
- [Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates](https://mlsanity.com/paper/2506.12459/) (2025)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks](https://mlsanity.com/paper/2405.02357/) (2024)
- [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://mlsanity.com/paper/2502.17812/) (2025)
- [A Survey on Mathematical Reasoning and Optimization with Large Language Models](https://mlsanity.com/paper/2503.17726/) (2025)
- [POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning](https://mlsanity.com/paper/2502.10038/) (2025)
- [Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review](https://mlsanity.com/paper/2506.01364/) (2025)
- [UniCL: A Universal Contrastive Learning Framework for Large Time Series Models](https://mlsanity.com/paper/2405.10597/) (2024)
- [Had enough of experts? Quantitative knowledge retrieval from large language models](https://mlsanity.com/paper/2402.07770/) (2024)
- [TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation](https://mlsanity.com/paper/2411.16020/) (2024)
- [Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales](https://mlsanity.com/paper/2412.18535/) (2024)
- [Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement](https://mlsanity.com/paper/2601.21149/) (2026)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models](https://mlsanity.com/paper/2505.11654/) (2025)
- [Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms](https://mlsanity.com/paper/2603.22332/) (2026)
- [ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2509.13753/) (2025)
- [Vision-LLMs for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/2510.11282/) (2025)
- [Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis](https://mlsanity.com/paper/2503.04870/) (2025)

## Abstract (arXiv)

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.