AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU
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DOI码：10.1155/2021/7751484
发表刊物：Security and Communication Network
摘要：Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). )e AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. )e experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient (R2), and explained variance score (EVS), and has the ability of long-term prediction.
全部作者：Xiangxiang Gu,Li Yang,Chengsheng Pan
第一作者：Huaifeng Shi
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：2021
期号：1
页面范围：7751484
是否译文：否
发表时间：2021-09-01
收录刊物：SCI
发表时间：2021-09-01