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Paper Abstract and Keywords
Presentation 2019-11-21 10:45
Investigation of The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning
Yusuke Tokuyama, Yukinobu Fukushima, Yuya Tarutani, Tokumi Yokohira (Okayama Univ.) NS2019-122
Abstract (in Japanese) (See Japanese page) 
(in English) It is crucial for network operators to predict network traffic in the future as accurate as possible for appropriate resource provisioning and traffic engineering.
In conventional studies, recurrent neural network (RNN) methods are considered to be the most promising prediction methods because of their high prediction accuracy.
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 on prediction accuracy in network traffic prediction using RNN methods.
Experimental results show that both timestamp and day of the week information are effective for improving the prediction accuracy, especially day of the week information significantly improves the prediction accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Network Traffic Prediction / Deep Learning / Recurrent Neural Network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 297, NS2019-122, pp. 13-18, Nov. 2019.
Paper # NS2019-122 
Date of Issue 2019-11-14 (NS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2019-122

Conference Information
Committee NS ICM CQ NV  
Conference Date 2019-11-21 - 2019-11-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Rokkodai 2nd Campus, Kobe Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence, etc. 
Paper Information
Registration To NS 
Conference Code 2019-11-NS-ICM-CQ-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning 
Sub Title (in English)  
Keyword(1) Network Traffic Prediction  
Keyword(2) Deep Learning  
Keyword(3) Recurrent Neural Network  
1st Author's Name Yusuke Tokuyama  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Yukinobu Fukushima  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Yuya Tarutani  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Tokumi Yokohira  
4th Author's Affiliation Okayama University (Okayama Univ.)
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Date Time 2019-11-21 10:45:00 
Presentation Time 25 
Registration for NS 
Paper # IEICE-NS2019-122 
Volume (vol) IEICE-119 
Number (no) no.297 
Page pp.13-18 
#Pages IEICE-6 
Date of Issue IEICE-NS-2019-11-14 

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