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Network Traffic Forecasting Using IFA-LSTM

  • Xianbin Han
  • Feng QiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

A network traffic prediction model is built based on LSTM neural network optimized with the improved firefly algorithm (IFA-LSTM). Aiming at some disadvantages of firefly algorithm including slow convergence, and easy to fall in local optimal values, we introduce a location update strategy based on the diversity of population, to avoid the optimization to fall into local optimal values. A dynamic step length updating measure is proposed to improve accuracy of the optimization, and to avoid the optimal solutions’ oscillation problem. Simulation examples show that the prediction accuracy and convergent speed of the IFA-LSTM method are obviously improved,it can be used to predict network traffic.

Keywords

Network traffic Firefly algorithm LSTM 

Notes

Acknowledgment

This work was supported by State Grid Technology Project ‘Edge Computing Research in Smart Grid Application and Security’ (Grant. 52110118001H); National Natural Science Foundation of China (Grant. 61702048).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Network TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

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