UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction

Author:

Yuan Yuan1ORCID,Ding Jingtao1ORCID,Feng Jie1ORCID,Jin Depeng1ORCID,Li Yong1ORCID

Affiliation:

1. Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

ACM

Reference76 articles.

1. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815.

2. Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros. 2023. Sequential modeling enables scalable learning for large vision models. arXiv preprint arXiv:2312.00785 (2023).

3. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

4. Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. 2023. Tempo: Prompt-based generative pre-trained transformer for time series forecasting. arXiv preprint arXiv:2310.04948 (2023).

5. Mau: A motion-aware unit for video prediction and beyond;Chang Zheng;Advances in Neural Information Processing Systems,2021

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