Abstract:

Spatio-temporal prediction task is widely applied in neuroscience， transportation， meteorology and other fields. As a typical spatio-temporal prediction task， temperature prediction needs to dig out the inherent spatio-temporal characteristics of temperature data. Aiming at the problems of large prediction error and insufficient spatial feature extraction in the existing temperature prediction algorithms， a temperature prediction model based on Graph Convolutional Network and Gated Recurrent Unit （GCN-GRU） was proposed. Firstly， the methods of weight redistribution and multi-order neighbor connection were used to modify Graph Convolutional Network （GCN） in order to effectively mine the unique spatial characteristics of the meteorological data. Secondly， the matrix multiplication of each recurrent unit in the Gated Recurrent Unit （GRU） was replaced by graph convolution operation， and all the recurrent units were connected in series to form a graph convolutional gating layer. Then， the graph convolutional gating layer was used to build the main network structure to extract the spatio-temporal characteristics of the data. Finally， the temperature prediction results were output through a fully connected output layer. Compared with the single models such as Gated Recurrent Unit （GRU） and Long Short-Term Memory （LSTM）， GCN-GRU had the Mean Absolute Error （MAE）reduced by 0.67 and 0.83 respectively； compared with the prediction model combined with Chebyshev graph convolution and Long Short-Term Memory （Cheb-LSTM） and the prediction model combined with Graph Convolutional Network and Long Short-Term Memory （GCN-LSTM）， the proposed model had the MAE reduced by 0.36 and 0.23 respectively.

Key words: spatio-temporal prediction, temperature prediction, multi-site, spatio-temporal characteristic, Graph Convolutional Network (GCN), Gated Recurrent Unit (GRU)

摘要：

时空预测任务在神经科学、交通、气象等领域应用广泛。气温预测作为典型的时空预测任务，需要挖掘气温数据中固有的时空特征。针对现有气温预测算法存在预测误差大、空间特征提取不充分的问题，提出一种基于图卷积网络和门控循环单元的气温预测（GCN-GRU）模型。首先，使用重新分配权重和多阶近邻连接方式修正图卷积网络（GCN），以有效挖掘气象数据独特的空间特征；然后，将门控循环单元（GRU）中每个循环单元的矩阵乘法替换成图卷积操作，并将所有的循环单元串联起来构成图卷积门控层；接着，使用图卷积门控层搭建网络主体结构来提取数据的时空特征；最后，通过一个全连接的输出层输出气温预测结果。通过与GRU和长短期记忆网络（LSTM）等单一模型对比，GCN-GRU模型的平均绝对误差（MAE）分别减小了0.67和0.83；与切比雪夫图卷积和长短期记忆网络结合的预测模型（Cheb-LSTM）、图卷积网络和长短期记忆网络结合的预测模型（GCN-LSTM）对比，平均绝对误差分别减小了0.36和0.23。

关键词: 时空预测, 气温预测, 多站点, 时空特征, 图卷积网络, 门控循环单元