An Autoregressive Graph Convolutional Long Short-Term Memory Hybrid Neural Network for Accurate Prediction of COVID-19 Cases

被引：3
|
作者：
Ntemi, Myrsini [1 ]
Sarridis, Ioannis [1 ]
Kotropoulos, Constantine [1 ]
机构：
[1] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece
来源：
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS | 2023年 / 10卷 / 02期
关键词：
Time series analysis; COVID-19; Pandemics; Forecasting; Predictive models; Transforms; Tensors; graph convolutional neural networks (GCNs); pandemic; TIME-SERIES;
D O I：
10.1109/TCSS.2022.3167856
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Efficient prediction of COVID-19 cases could prepare the healthcare system to accommodate the COVID-19 cases in the forthcoming days and improve the overall resource management. A hybrid model comprised of an autoregressive filter, a graph convolutional neural network (GCN), and a long short-term memory neural network is proposed for COVID-19 cases prediction in USA. It captures accurately both linearities and nonlinearities present in the time series. An adjacency matrix is exploited in GCN that relies on Granger causality tests applied to historical COVID-19 cases for each state in USA. By doing so, the latent information about the spread of the virus is captured efficiently and the prediction performance of the hybrid model is improved, revealing which state truly affects the other ones. The proposed method outperforms the state-of-the-art techniques.
引用
收藏
页码：724 / 735
页数：12
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