MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
Con- necting the dots: Multivariate time series forecasting with graph neural networks,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3verdicts
UNVERDICTED 3representative citing papers
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
citing papers explorer
-
Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
-
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
-
Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.