UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
<|reference_start|>UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction: Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging Existing prediction approaches are typically tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive domain-specific training data. In this study, we introduce UniST, a universal model designed for general urban spatio-temporal prediction across a wide range of scenarios. Inspired by large language models, UniST achieves success through: (i) utilizing diverse spatio-temporal data from different scenarios, (ii) effective pre-training to capture complex spatio-temporal dynamics, (iii) knowledge-guided prompts to enhance generalization capabilities. These designs together unlock the potential of building a universal model for various scenarios Extensive experiments on more than 20 spatio-temporal scenarios demonstrate UniST's efficacy in advancing state-of-the-art performance, especially in few-shot and zero-shot prediction. The datasets and code implementation are released on https://github.com/tsinghua-fib-lab/UniST.<|reference_end|>
@article{yuan2024unist:,
    title={UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal
  Prediction},
    author={Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li},
    journal={arXiv preprint arXiv:2402.11838},
    year={2024},
    doi={10.1145/3637528.3671662},
    archivePrefix={arXiv},
    eprint={2402.11838},
    primaryClass={cs.LG}
}