其中,Gconv(⋅)是一个图卷积层。图卷积递归网络(Graph Convolutional Recurrent Network, GCRN)[71] 将LSTM网络与ChebNet [21] 结合在一起。扩散卷积递归神经网络(Diffusion Convolutional Recurrent Neural Network, DCRNN)[72] 将提出的扩散图卷积层(方程18)结合到GRU网络中。此外,DCRNN采用了编码器-解码器框架来...
This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the...
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have become an emerging research issue for improving the traffic forecasting problem. Specifically, one of the main types of GNNs is the spatial-temporal GNN (ST-GNN), which has been applied to various time-series...
或者是:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting GitHub:https://github.com/zezhishao/STEP KDD 2022的论文。 摘要 多变量时间序列(MTS)预测在广泛的应用中发挥着重要作用。最近,时空图神经网络(STGNNs)日益成为流行的 MTS 预测方法。STGNNs 通过图神经...
或者是:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting GitHub:https://github.com/zezhishao/STEP KDD 2022的论文。 摘要 多变量时间序列(MTS)预测在广泛的应用中发挥着重要作用。最近,时空图神经网络(STGNNs)日益成为流行的 MTS 预测方法。STGNNs 通过图神经...
The pioneering work27 concerning spatial-temporal graph convolutional networks (ST-GCNs), which encapsulate human skeleton data within graph frameworks, is particularly important. In this approach, a GCN is used for skeleton-based action recognition. This impetus has pushed GCN-based methods to the ...
In this paper, we propose a graph neural network framework, namely Spatial-Temporal Graph Social Network (STGSN), which models social networks from both spatial and temporal perspectives. Using a novel approach, we leverage the temporal attention mechanism to capture social networks' temporal ...
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
In recent years, Spatial-Temporal Graph Neural Networks (STGNNs) has faced increasing challenges in traffic flow forecasting. The main issue lies in the significant indistinguishability among traffic nodes, where highly similar inputs correspond to completely different outputs, posing a major challenge...
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods. STGNNs jointly model the spatial and temporal patterns of MTS through graph neural networks and sequential models, significantly improving the prediction accuracy. But limited by model ...