Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 186-193.DOI: 10.3778/j.issn.1002-8331.1812-0278

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Application of Gaussian Process Mixture Model on  Network Traffic Prediction

LI Song, ZHOU Yatong, CHI Yue, HE Jingfei,  ZHANG Shili   

  1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
  • Online:2020-03-01 Published:2020-03-06



  1. 河北工业大学 电子信息工程学院,天津 300401


Accurate network traffic prediction can avoid network crashes and ensure network fluency. The paper uses the Gaussian Process Mixture(GPM) model for multi-modal prediction of network traffic. Firstly, the multi-modal analysis of the network traffic sequences in two different regions is carried out, and then normalized and phase-space reconstructed to generate a sample set and input into the GPM model. Finally, the classification iterative learning algorithm is used to realize the model parameter learning by using the posterior probability maximization and likelihood function. The GPM model is compared with models such as Support Vector Machine(SVM), Kernel Regression(KR), Minimum and maximum Probability Machine Regression(MPMR), and Gaussian Process(GP). By comparing the Root Mean Square Error(RMSE) and the decision coefficient(R2) evaluation index, the prediction accuracy of the GPM model is better than the other four models. The GPM model can be well applied to network traffic prediction and can provide reference for network administrators to allocate network resources.

Key words: network traffic, prediction, Gaussian process mixture model, multimodal



关键词: 网络流量, 预测, 高斯过程混合模型, 多模态