Research on network traffic prediction based on spatial feature fusion

Forecasting network traffic is critical to operators' daily network operating and maintenance decisions. However, nonlinearity, burst, and periodicity of network traffic, as well as the considerable geographical correlation among network nodes, pose significant hurdles to reliable network traffic forecast. Most existing traffic prediction methods use pre-defined graph or node embedding to extract spatial correlation between network nodes. However, neither of these methods may be able to extract this spatial correlation completely. Furthermore, while utilizing LSTM to extract temporal features, intermediate time step output is ignored, resulting in the loss of some temporal features. The network model in the article extracts the spatial correlation by the dual graphic attention module and extracts temporal features by the LSTM-Attention and TCN modules, which can extract spatial and temporal features from the network traffic data better. The network model is trained and predicted on the Abilene data set. The results demonstrate that the prediction performance is significantly improved.