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A Trend Filtering Based Prediction Model on Network Traffic

  • Hui XiaEmail author
  • Bin Fang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Currently, the most effective traffic prediction methods are based on ANNs (Artificial Neural Networks), for their great ability to process complex datasets. However, ANNs-based prediction models require large training datasets to identify parameters and optimize structures. Furthermore, more time on training limits their real-time applications. In this paper, we develop a modification of Hodrick-Prescott (HP) trend filtering model with the constraints of local structure and periodical property of network traffic. We preserve the locality by smoothing the neighboring traffic measurements, and maintain the periodicity by keeping the similarity of daily and weekly traffic series. Then the generated trend intensifies the characteristics of network traffic and is easier to be learned. Moreover, we predict traffic volumes through the prediction of trend such that both the number of training samples and the corresponding training time being reduced. We process two datasets in our experiments. The results show that our method performs better than BP (Back Propagation)-based prediction algorithm by 24.5% in MAE (Mean Absolute prediction Error).

Keywords

Traffic prediction Trend extraction Behavior patterns 

Notes

Acknowledgement

This work was supported by the Scientific Research Project of ChongQing Board of Education Committee (KJQN201801103), and the Doctoral Program of Chongqing Federation of Social Science (2018BS68).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chongqing University of TechnologyChongqingChina
  2. 2.Chongqing UniversityChongqingChina

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