PDF(Pubmed)
Abstract：
UNASSIGNED: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.
UNASSIGNED: The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).
UNASSIGNED: For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model\'s robustness.
UNASSIGNED: GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.
UNASSIGNED: The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).
UNASSIGNED: For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model\'s robustness.
UNASSIGNED: GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.
摘要：
■这项研究的重点是通过将门控递归单元（GRU）集成到图神经网络（GNN）中来提高流行病时间序列数据预测的精度，形成GRGNN。通过与七种常用的预测方法进行比较，验证了引入GRU（门控递归单元）的GNN（图形神经网络）网络的准确性。
■GRGNN方法涉及使用通过GRU（门控递归单位）的积分改进的GNN（图形神经网络）网络的多变量时间序列预测。此外,介绍了图形傅里叶变换(GFT)和离散傅里叶变换(DFT)。GFT捕获频谱域中的序列间相关性，而DFT将数据从时域转换到频域，揭示时间节点相关性。在GFT和DFT之后，疫情数据通过频域中的一维卷积和门控线性回归进行预测，频谱域中的图卷积，和时域中的GRU(门控递归单位)。采用GFT和DFT的逆变换，并在通过全连接层后获得最终预测。对三个数据集进行评估：38个非洲国家和42个欧洲国家的COVID-19数据集，和来自Kaggle的20个匈牙利地区的水痘数据集。度量包括平均均方根误差(ARMSE)和平均平均绝对误差(AMAE)。
■对于非洲COVID-19数据集和匈牙利水痘数据集，在各种预测步长上，GRGNN始终优于ARMSE和AMAE中的其他方法。即使在扩展的预测步骤中，也可以获得最佳结果，突出模型的健壮性。
■GRGNN被证明在预测流行病时间序列数据方面具有很高的准确性，展示其在流行病监测和预警应用中的潜力。然而,需要进一步的讨论和研究，以完善其应用和判断方法，强调在这一领域进行探索和研究的持续需要。
■GRGNN方法涉及使用通过GRU（门控递归单位）的积分改进的GNN（图形神经网络）网络的多变量时间序列预测。此外,介绍了图形傅里叶变换(GFT)和离散傅里叶变换(DFT)。GFT捕获频谱域中的序列间相关性，而DFT将数据从时域转换到频域，揭示时间节点相关性。在GFT和DFT之后，疫情数据通过频域中的一维卷积和门控线性回归进行预测，频谱域中的图卷积，和时域中的GRU(门控递归单位)。采用GFT和DFT的逆变换，并在通过全连接层后获得最终预测。对三个数据集进行评估：38个非洲国家和42个欧洲国家的COVID-19数据集，和来自Kaggle的20个匈牙利地区的水痘数据集。度量包括平均均方根误差(ARMSE)和平均平均绝对误差(AMAE)。
■对于非洲COVID-19数据集和匈牙利水痘数据集，在各种预测步长上，GRGNN始终优于ARMSE和AMAE中的其他方法。即使在扩展的预测步骤中，也可以获得最佳结果，突出模型的健壮性。
■GRGNN被证明在预测流行病时间序列数据方面具有很高的准确性，展示其在流行病监测和预警应用中的潜力。然而,需要进一步的讨论和研究，以完善其应用和判断方法，强调在这一领域进行探索和研究的持续需要。
