Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

In order to improve the prediction accuracy of network traffic, a novel prediction model based on ensemble empirical mode decomposition and multiple models is proposed. In this study, ensemble empirical mode decomposition algorithm is introduced to decompose the network traffic and get several components. Approximate entropy is introduced to judge the complexity of each component. According to the results of approximate entropy, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is introduced to predict low complexity components. The advantages of each prediction model are used to predict the appropriate component. The final prediction results are obtained by adding the predicted values of each component. In order to solve the problem that the prediction performance of support vector machine and echo state network is affected by their parameters, an improved whale optimization algorithm is proposed to optimize the parameters of the model. Meanwhile, the calculation results of approximate entropy show that compared with the original network traffic, the complexity of each component obtained by ensemble empirical mode decomposition is reduced, which reduces the complexity of modeling. Three network traffic datasets with sampling periods of 10 ms, 1 s, and 10 min are collected. Compared with the other five state-of-the-art prediction models, the case study results show that the proposed prediction model has better prediction accuracy and excellent statistical performance indicators.

Automated summary

A novel prediction model based on ensemble empirical mode decomposition and multiple models is proposed to improve the prediction accuracy of network traffic. The complexity of each component is judged by introducing approximate entropy, and different prediction models are used to predict components with different complexities. An improved whale optimization algorithm is utilized to optimize model parameters for enhancing prediction accuracy. Experimental results demonstrate that the proposed model outperforms other models in terms of prediction accuracy and statistical performance indicators.