Abstract:

Accurate prediction of network traffic variations can help operators allocate resources and schedule in advance,thus minimizing network congestion.Existing multi-step prediction methods for network traffic struggle to capture the long-range dependencies in traffic sequences,resulting in a low accuracy in multi-step prediction tasks.In response,a novel method using time two-dimensional-variation modeling for multi-step network traffic prediction is proposed which first encodes the network traffic sequence using Gated Recurrent Units(GRUs) to accurately represent the temporal correlations of network traffic and then reconstructs the traffic based on its periodic characteristics,transforming the one-dimensional traffic sequence into two dimensions.The reconstructed traffic sequence has a compressed length and more concentrated features,enabling the model to effectively perceive its long-range dependencies.Finally,a novel convolutional neural network captures the two-dimensional features of the reconstructed traffic sequence and performs weighted fusion to produce the final prediction results.Simulation results show that compared to mainstream multi-step network traffic prediction methods,the proposed method reduces the root mean square error by at least 8.69%,mean absolute error by at least 8.96%,and the mean absolute percentage error by at least 11.73%.Experimental results demonstrate that the proposed method can effectively mine long-range dependencies in network traffic,achieving a higher accuracy in multi-step traffic prediction tasks.

Key words: forecasting, network management, traffic prediction, temporal 2D-variation modeling