Abstract: Network traffic prediction, as a critical technology, can assist in achieving rational allocation of network resources, optimizing network performance, and providing efficient network services. With the evolution and development of network environments, the diversity and complexity of network traffic have increased. To improve the accuracy of network traffic prediction, a network traffic prediction model based on improved northern goshawk optimization for stochastic configuration network (CNGO-SCN) is proposed. Stochastic configuration network, as a supervised incremental model, has significant advantages in addressing large-scale data regression and prediction problems. However, the accuracy of the stochastic configuration network is influenced by the selection of some hyperparameters. To address this issue, the northern goshawk optimization algorithm is used to optimize the regularization parameters and scaling factors that affect the performance of the stochastic configuration network, obtaining the optimal values. As the initial distribution of the population in the northern goshawk optimization algorithm leads to poor individual quality, chaos logic mapping is introduced to improve the quality of initial solutions. The optimized model is applied to real traffic datasets from the UK academic network, the core network of a European city, and a network collaborative manufacturing cloud platform interface established by a cooperative enterprise. It is compared with various neural network models to verify the network traffic prediction capability of the proposed method. Experimental results show that the model has higher prediction accuracy compared to other neural networks, exhibiting superior predictive capability when dealing with complex data in practical scenarios. The prediction error of the model decreases by 0.9% to 99.7%.

Key words: network traffic prediction, stochastic configuration network, northern goshawk optimization algorithm, chaotic logic mapping