单时序特征图卷积网络融合预测方法

近年来，图神经网络逐渐成为深度学习领域广泛讨论的话题和研究的重点，但大多数研究都是基于图节点，在存在多维属性的前提下进行分类和回归预测，对单时序特征的图节点预测并不能产生理想的效果。本文提出一种时序图卷积网络算法，可以在复杂图网络中，只根据节点单一特征的时序序列，实现对该特征的预测。算法通过在传统图卷积网络中对邻接矩阵参数化，解决单一特征条件下的参数退化问题，并结合长短时记忆网络的序列学习方法，将时序信息融入到训练过程中，提高训练精度。在交通流量数据集PeMS和Los上的实验表明，其预测精度要优于GCN、T-GCN、GRU、LSTM等主流算法。

A Hybrid Prediction Method on Graph Convolutional Network with Single Time-series Feature

In recent years, graph neural networks have been widely discussed in the field of deep learning. However, most of the researches are based on graph nodes and carry out classification and regression prediction under the premise of multi-dimensional attributes. Forecasting does not produce the desired results on single time-series of feature. This paper proposes a time-series graph convolutional network that can predict features in a complex graph network based on single time-series of feature of the node. By parameterizing the adjacency matrix in the traditional graph convolution network, the algorithm solves the problem of parameter degradation under a single feature condition, and combines the sequential learning method of the LSTM network to integrate the timing information into the training process, which improves the training accuracy. Experiments on the traffic flow data set PeMS and Los show that the prediction accuracy is better than that of mainstream algorithms such as GCN, T-GCN, GRU, LSTM.