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...
https://blog.twitter.com/engineering/en_us/topics/insights/2021/temporal-graph-networks背景介绍GNN 最近在生物学、化学、社会科学、物理学和许多其他领域的问题上取得了一系列成功。到目前为止,GNN 模型主…
2. Heterogeneous Temporal Graph Neural Network (HTGNN) 2.1 深度学习与图神经网络简介 深度学习是一种机器学习方法,它通过多层神经网络模拟人类大脑的工作原理来进行模式识别和预测。图神经网络是一种特殊类型的深度学习模型,用于处理图数据。传统上,深度学习主要用于处理向量化的数据,例如图像和文本等。然而,在许多现...
论文:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting 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...
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...
PyTorch Geometric Temporal is a temporal graph neural network extension library forPyTorch Geometric. PyTorch Geometric Temporal 是基于PyTorch Geometric的对时间序列图数据的扩展。 Data Structures: PyTorch Geometric Temporal Signal 定义:在PyTorch Geometric Temporal中,边、边特征、节点被归为图结构Graph,节点特征...
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transfor...
Motivated by the aforementioned observations, in this article, we propose a new deep Spatial Temporal Graph neural Network S (STGNets for abbreviation) for predicting energy consumption in the process industry. As shown in Fig. 1, our model framework consists of three primary modules. Implementing...
We investigate the impact of different graph neural network topologies on prediction performance, including distance-based and fully connected graphs. Moreover, we propose a composite model that predicts the PV energy output for any node within the stations' network, enabling localized and accurate ...
Subsequently, a continuous graph neural network (CGNN)35 is derived to alleviate the oversmoothing problem. Taking advantage of this strategy, an ODE-temporal convolutional network (TCN) module is developed to enhance the temporal modeling ability of the model so that it can simulate long-term ...