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| Graph neural network model for multivariate time series forecasting |
Han ZHANG( ) |
| School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian 116025, China |
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Abstract Most of the existing graph neural network models for forecasting multivariate time series capture the time series characteristics in a static way based on predefined graphs, and may be lack of capturing the dynamic adaptation of the system and some potential dynamic relationships between time series samples. A graph neural network model for multivariate time series prediction (MTSGNN) was proposed. In a graph learning module, the static and dynamic evolution graphs of time series data were learned in a data-driven way to capture the complex relationships between time series samples. The information interaction between the static and dynamic graphs was realized by the graph interaction module, and the convolution operation was used to extract the dependency in the interaction information. A multi-layer perceptron was used to forecast the multivariate time series. Experimental results on six real multivariate time series datasets showed that the forecasting effect of the proposed model was significantly better than those of the current state-of-the-art methods, and it had the advantages of small parameter quantity and fast operation speed.
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Received: 30 December 2023
Published: 25 November 2024
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| Fund: 辽宁省应用基础研究计划资助项目(2023JH2/101600040)；辽宁省教育厅基本科研资助项目(LJKMZ20221598). |
用于多元时间序列预测的图神经网络模型
现有用于多元时序预测的图神经网络模型大多基于预定义图以静态的方式捕捉时序特征，缺少对于系统动态适应和对时序样本之间潜在动态关系的捕捉. 提出用于多元时序预测的图神经网络模型 (MTSGNN). 该模型在一个图学习模块中，采用数据驱动的方式学习时间序列数据的静态图和动态演化图，以捕捉时序样本之间的复杂关系. 通过图交互模块实现静态图和动态图之间的信息交互，并使用卷积运算提取交互信息中的依赖关系. 利用多层感知机对多元时序进行预测. 实验结果表明，所提模型在6个真实的多元时间序列数据集上的预测效果显著优于当前最先进的方法，并且具有参数量较小、运算速度较快的优点.
关键词：
多元时间序列,
图神经网络,
静态图,
动态图,
图交互
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