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

Abstract

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.

2024 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2024

References in corpus (3)

• Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
• Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

Cited by in corpus (6)

• Foundation Models for Time Series Analysis: A Tutorial and Survey
• An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
• OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
• Urban Computing in the Era of Large Language Models
• Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs
• UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization