Abstract: In order to improve the accuracy of network traffic prediction, a network traffic prediction model is proposed based on wavelet denoising and improved slime mold algorithm optimized support vector machine. First, wavelet denoising is used to denoise network traffic, and support vector machine is used as the prediction model. Since the prediction results of support vector machine are greatly affected by the model parameters, an improved slime mold algorithm with random inertia weight is used to optimize the penalty factor and kernel function parameters that used in the support vector machine model. The validity of the proposed model is verified by the collected network traffic. The simulation results show that the proposed model is superior to the comparison model in terms of the evaluation index.

Key words: network traffic, prediction, wavelet denoising, support vector machine, improved slime mould algorithm