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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) …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
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 …
Learning graph ode for continuous-time sequential recommendation
Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …
behavior correlations, which are usually represented as the item purchasing sequences …
Lightcts: A lightweight framework for correlated time series forecasting
Correlated time series (CTS) forecasting plays an essential role in many practical
applications, such as traffic management and server load control. Many deep learning …
applications, such as traffic management and server load control. Many deep learning …
Explainable spatio-temporal graph neural networks
Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool
for effectively modeling spatio-temporal dependencies in diverse real-world urban …
for effectively modeling spatio-temporal dependencies in diverse real-world urban …
Cross-city few-shot traffic forecasting via traffic pattern bank
Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
Unveiling delay effects in traffic forecasting: a perspective from spatial-temporal delay differential equations
Traffic flow forecasting is a fundamental research issue for transportation planning and
management, which serves as a canonical and typical example of spatial-temporal …
management, which serves as a canonical and typical example of spatial-temporal …
AdpSTGCN: Adaptive spatial–temporal graph convolutional network for traffic forecasting
X Chen, H Tang, Y Wu, H Shen, J Li - Knowledge-Based Systems, 2024 - Elsevier
Traffic flow forecasting plays a crucial role in applications such as intelligent transportation
systems. Despite significant research in this field, the current methods have limitations that …
systems. Despite significant research in this field, the current methods have limitations that …
Self-supervised contrastive representation learning for large-scale trajectories
Trajectory representation learning aims to embed trajectory sequences into fixed-length
vector representations while preserving their original spatio-temporal feature proximity …
vector representations while preserving their original spatio-temporal feature proximity …