An EMD- and GRU-based hybrid network traffic prediction model with data reconstruction

Network traffic can reflect the operating status and resource bottleneck of the entire network. Accurate prediction of the future network is helpful in network maintenance, network optimization, routing policy design, load balancing, protocol design, and anomaly detection. However, the self-similarity, periodicity, chaos, multi-scale, and other characteristics of modern network traffic make it challenging to predict network behaviors. The available prediction models focus only on self-similarity and burstiness, lacking a more accurate and comprehensive description of the characteristics of network traffic. In this paper, we propose a prediction model based on Empirical Mode Decomposition (EMD) and the Gated Recurrent Unit (GRU) neural network with data reconstruction. First, the traffic data are reconstructed by complementing missing-points and eliminating outliers. Then, we decompose the reconstructed traffic data into several components through EMD and use each component to train the corresponding GRU neural network. Finally, the predicted values of all components are combined to get the final result. Numerical results show that the proposed prediction model offers higher accuracy and more stable performance than the state-of-the-art models.