Graph Neural Network Model Based on Transfer Entropy for Agricultural Futures Forecasting

doi:10.3778/j.issn.1002-8331.2108-0224

Abstract

Abstract: In view of the nonlinear fluctuation of agricultural futures price and the linkage characteristics of domestic and foreign futures products, considering that the traditional neural network prediction model can not quantitatively characterize the causal relationship between multi-source input variables, this paper constructs a graph neural network prediction model with transfer entropy. Firstly, the adjacency matrix between nodes is represented by calculating the transfer entropy, which is used as a priori information to identify the causal relationship between variables. At the same time, the temporal convolution module of multi-scale filter is set to extract the node features to identify the time dependence of the sequence. Secondly, the graph convolution module is set to realize the propagation and feature selection of node information and its neighborhood information. Finally, the final prediction result is output by connecting parameters. The empirical study on soybean futures data shows that compared with the existing general forecasting model, this model can achieve the best forecasting effect.

Key words: agricultural products futures forecasting, graph neural network, transfer entropy, multivariate time series

摘要： 针对农产品期货价格波动的非线性及国内外期货产品的联动性特征，考虑到传统神经网络预测模型未能针对多源输入变量间的因果关系进行定量表征，构建融合传递熵的图神经网络预测模型。通过计算传递熵表示节点间的邻接矩阵，作为先验信息识别变量间的因果关系；设置多尺寸滤波器的时间卷积模块提取节点特征，用于识别序列时间依赖性；设置图卷积模块实现对节点信息及其邻域信息的传播与特征筛选，最后连接参数，输出最终的预测结果。在大豆期货数据上的实证研究表明，相较于现有的通用预测模型，该模型能够实现最佳的预测效果。

关键词: 农产品期货预测, 图神经网络, 传递熵, 多元时间序列

ZHANG Jie, ZHEN Liulin, XU Shuo, ZHAI Dongsheng. Graph Neural Network Model Based on Transfer Entropy for Agricultural Futures Forecasting[J]. Computer Engineering and Applications, 2023, 59(2): 321-328.

张杰, 甄柳琳, 徐硕, 翟东升. 融合传递熵的图神经网络农产品期货预测模型[J]. 计算机工程与应用, 2023, 59(2): 321-328.

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