Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks

Yusuke Tokuyama, Ryo Miki, Yukinobu Fukushima, Yuya Tarutani, Tokumi Yokohira

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

Abstract

Recurrent neural network method considering traffic volume, timestamp and day of the week (RNN-VTD method) is a promising network traffic prediction method because of its high prediction accuracy. The RNN-VTD method encodes timestamp and day of the week, which are categorical data, to numerical data using label encoding. The label encoding, however, gives magnitude to the encoded values, which may cause misunderstanding of recurrent neural network models, and consequently, the prediction accuracy of the RNN-VTD method may be degraded. In this paper, we investigate the effect of using one-hot encoding instead of label encoding for a feature encoding method in the RNN-VTD method. In the one-hot encoding, each input data is encoded to an k-dimensional 0 - 1 vector where k is the number of category types. Because the encoded data do not have magnitude, it is expected that the prediction accuracy of the RNN-VTD method is improved.

Original languageEnglish
Title of host publicationProceedings of the 2020 8th International Conference on Information and Education Technology, ICIET 2020
PublisherAssociation for Computing Machinery
Pages279-283
Number of pages5
ISBN (Electronic)9781450377058
DOIs
Publication statusPublished - Mar 28 2020
Event8th International Conference on Information and Education Technology, ICIET 2020 - Okayama, Japan
Duration: Mar 28 2020Mar 30 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Information and Education Technology, ICIET 2020
CountryJapan
CityOkayama
Period3/28/203/30/20

Keywords

  • Feature Encoding
  • Network traffic prediction
  • Recurrent Neural Networks

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Tokuyama, Y., Miki, R., Fukushima, Y., Tarutani, Y., & Yokohira, T. (2020). Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks. In Proceedings of the 2020 8th International Conference on Information and Education Technology, ICIET 2020 (pp. 279-283). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3395245.3396441