A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
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) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
Long-range transformers for dynamic spatiotemporal forecasting
Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
Hope: High-order graph ode for modeling interacting dynamics
Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
Generic dynamic graph convolutional network for traffic flow forecasting
In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are
emerging. But existing methods still have limitations due to insufficient sharing patterns …
emerging. But existing methods still have limitations due to insufficient sharing patterns …
ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer
The rapid development of road traffic networks has provided a wealth of research data for
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
Self-supervised graph-level representation learning with adversarial contrastive learning
The recently developed unsupervised graph representation learning approaches apply
contrastive learning into graph-structured data and achieve promising performance …
contrastive learning into graph-structured data and achieve promising performance …