The prediction performance of many existing traffic prediction models is limited by the fixed original graph structure and inappropriate spatio-temporal dependency extraction. For this situation, this paper proposes aspatio-temporalgraphneuralnetwork based onadaptiveneighborhoodselection (STGNN-ANS). To ...
KienMN / STGNN-for-Covid-in-Korea Star 28 Code Issues Pull requests Spatio-temporal graph neural network for predicting COVID-19 new cases in Korea. timeseries forecasting graph-neural-networks covid-19 spatio-temporal-graphs Updated Dec 8, 2021 Python Kumbong / GraphWavenet Star 0 Co...
Spatio-Temporal Graph neural network (STGNN). Trainer Trainer contains every information (such as dataset, optimizer, loss function, etc) for training each type of models mentioned above. Callbacks This module consists of callbacks which can be executed before/after some steps of training or testing...
17,18,19. The graph structure in GNN20can efficiently gather spatiotemporal information and update the graph by employing the graph's adjacency matrix and the graph's convolution operation to fuse the information of neighboring nodes. The standard graph neural network only considers static...
The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs) ...
Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as t...
Our empirical results demonstrate that the proposed STGNN significantly outperforms both the WMMSE benchmark and memoryless graph neural networks (GNNs) across all simulated scenarios, and converges to the globally optimal solution of the load-spillage algorithm, with lower computational complexity. ...
21-03-13 StemGNN🌟🔥 NIPS 2020 Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting StemGNN 22-05-16 TPGNN NIPS 2022 Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks TPGNN 22-06-18 D2STGNN VLDB 2022 Decoupled Dynamic Spatial-Temporal ...
21-03-13 StemGNN🌟 NIPS 2020 Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting StemGNN 22-05-16 TPGNN NIPS 2022 Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks TPGNN 22-06-18 D2STGNN VLDB 2022 Decoupled Dynamic Spatial-Temporal Graph...
To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation c...