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: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
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 …
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Convolutional neural networks on graphs with chebyshev approximation, revisited
Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
Transfer graph neural networks for pandemic forecasting
G Panagopoulos, G Nikolentzos… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
The recent outbreak of COVID-19 has affected millions of individuals around the world and
has posed a significant challenge to global healthcare. From the early days of the pandemic …
has posed a significant challenge to global healthcare. From the early days of the pandemic …
A survey on time-series pre-trained models
Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …
practical applications. Deep learning models that rely on massive labeled data have been …
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Achieving accurate and robust global situational awareness of a complex time-evolving field
from a limited number of sensors has been a long-standing challenge. This reconstruction …
from a limited number of sensors has been a long-standing challenge. This reconstruction …
STGAT: Spatial-temporal graph attention networks for traffic flow forecasting
Traffic flow forecasting is a critical task for urban traffic control and dispatch in the field of
transportation, which is characterized by the high nonlinearity and complexity. In this paper …
transportation, which is characterized by the high nonlinearity and complexity. In this paper …
Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning
X Zhou, Y Zhang, Z Li, X Wang, J Zhao… - Neural Computing and …, 2022 - Springer
Intelligent cellular traffic prediction is very important for mobile operators to achieve resource
scheduling and allocation. In reality, people often need to predict very large scale of cellular …
scheduling and allocation. In reality, people often need to predict very large scale of cellular …