A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection

M Jin, HY Koh, Q Wen, D Zambon… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Deciphering spatio-temporal graph forecasting: A causal lens and treatment

Y Xia, Y Liang, H Wen, X Liu, K Wang… - Advances in …, 2024 - proceedings.neurips.cc
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

Large models for time series and spatio-temporal data: A survey and outlook

M Jin, Q Wen, Y Liang, C Zhang, S Xue, X Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Deep learning for trajectory data management and mining: A survey and beyond

W Chen, Y Liang, Y Zhu, Y Chang, K Luo… - arXiv preprint arXiv …, 2024 - arxiv.org
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …

Unist: a prompt-empowered universal model for urban spatio-temporal prediction

Y Yuan, J Ding, J Feng, D Jin, Y Li - Proceedings of the 30th ACM …, 2024 - dl.acm.org
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook

X Zou, Y Yan, X Hao, Y Hu, H Wen, E Liu, J Zhang… - Information …, 2025 - Elsevier
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

Equivariant spatio-temporal attentive graph networks to simulate physical dynamics

L Wu, Z Hou, J Yuan, Y Rong… - Advances in Neural …, 2024 - proceedings.neurips.cc
Learning to represent and simulate the dynamics of physical systems is a crucial yet
challenging task. Existing equivariant Graph Neural Network (GNN) based methods have …

Spatial-temporal large language model for traffic prediction

C Liu, S Yang, Q Xu, Z Li, C Long, Z Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Traffic prediction, a critical component for intelligent transportation systems, endeavors to
foresee future traffic at specific locations using historical data. Although existing traffic …

Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning

Z Zhou, Q Huang, K Yang, K Wang, X Wang… - Proceedings of the 29th …, 2023 - dl.acm.org
Spatiotemporal (ST) learning has become a crucial technique for urban digitalization. Due to
expansions and dynamics of cities, current spatiotemporal models are inclined to suffer …

Large graph models: A perspective

Z Zhang, H Li, Z Zhang, Y Qin, X Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …