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). The 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. The 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 (
R
2
), and explained variance score (EVS), and has the ability of long-term prediction.
Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks
