The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

It is crucial for network operators to predict network traffic in the future as accurate as possible for appropriate resource provisioning and traffic engineering. Recurrent neural network (RNN) methods are considered to be the most promising prediction methods because of their high prediction accuracy. In conventional studies, RNN methods use only time series of traffic volume as input, and do not use any attribute information (e.g., timestamp and day of the week) of the time series data. However, traffic volume changes depending on both time and day of the week. Therefore, it is possible that we can further improve the prediction accuracy of the RNN methods by using the attribute information as input, in addition to the time series of traffic volume. In this paper, we investigate the effect of using the attribute information of time series of traffic volume on prediction accuracy in network traffic prediction. We propose two RNN methods: RNN-VT method and RNN-VTD method. The RNN-VT method uses timestamp information and the RNN-VTD method uses both timestamp and day of the week information as input, in addition to the time series of traffic volume. Experimental results show that day of the week information is significantly effective for improving prediction accuracy of the RNN methods while timestamp information is not effective.

Original languageEnglish
Title of host publication9th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Powered by Smart Intelligence, ICTC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages521-525
Number of pages5
ISBN (Electronic)9781538650400
DOIs
Publication statusPublished - Nov 16 2018
Event9th International Conference on Information and Communication Technology Convergence, ICTC 2018 - Jeju Island, Korea, Republic of
Duration: Oct 17 2018Oct 19 2018

Other

Other9th International Conference on Information and Communication Technology Convergence, ICTC 2018
CountryKorea, Republic of
CityJeju Island
Period10/17/1810/19/18

Keywords

  • Deep Learning
  • Network Traffic Prediction
  • Recurrent Neural Network
  • The Internet traffic

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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  • Cite this

    Tokuyama, Y., Fukushima, Y., & Yokohira, T. (2018). The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning. In 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 (pp. 521-525). [8539488] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2018.8539488