GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks

Published: 21 Sept 2023, Last Modified: 07 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: Spatial Temporal Prediction, Deep Neural Networks, Pre-training Model
TL;DR: A generative pre-training framework for improving the spatio-temporal prediction performance of downstream models.
Abstract: In recent years, there has been a rapid development of spatio-temporal prediction techniques in response to the increasing demands of traffic management and travel planning. While advanced end-to-end models have achieved notable success in improving predictive performance, their integration and expansion pose significant challenges. This work aims to address these challenges by introducing a spatio-temporal pre-training framework that seamlessly integrates with downstream baselines and enhances their performance. The framework is built upon two key designs: (i) We propose a spatio-temporal mask autoencoder as a pre-training model for learning spatio-temporal dependencies. The model incorporates customized parameter learners and hierarchical spatial pattern encoding networks. These modules are specifically designed to capture spatio-temporal customized representations and intra- and inter-cluster region semantic relationships, which have often been neglected in existing approaches. (ii) We introduce an adaptive mask strategy as part of the pre-training mechanism. This strategy guides the mask autoencoder in learning robust spatio-temporal representations and facilitates the modeling of different relationships, ranging from intra-cluster to inter-cluster, in an easy-to-hard training manner. Extensive experiments conducted on representative benchmarks demonstrate the effectiveness of our proposed method. We have made our model implementation publicly available at
Supplementary Material: pdf
Submission Number: 4032