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Paper Abstract and Keywords
Presentation 2012-04-12 13:30
Forecasting individual trends using resolution adaptive arima
Hiroki Nakayama, Shingo Ata, Ikuo Oka (Osaka City Univ.) IN2012-1
Abstract (in Japanese) (See Japanese page) 
(in English) Analyzing and predicting the time series of traffic trends is important in the perspective of network operation and management. However, it is difficult to accurately predict traffic trends because this strongly depends on the time, the type of content, and its popularity. We propose a new method based on wavelet transform and the auto regressive integrated moving average (ARIMA) model to predict traffic trends more accurately. We first demonstrate that applying a wavelet transform can improve the accuracy of prediction compared to the original ARIMA model; however, it still has large error due to the fixed time granularity of each resolution. We therefore propose a resolution adaptive ARIMA (RA-ARIMA) model which is based on dynamic time granularity and a Wavelet-ARIMA model. We demonstrate that by applying it to the real monitored data in a major P2P file-sharing system the mean squared error and normalized mean squared error of RA-ARIMA can be reduced by more than 80% of that of Wavelet-ARIMA.
Keyword (in Japanese) (See Japanese page) 
(in English) Demand-Prediction / Wavelet Transform / Time Series Analysis / ARIMA Models / / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 4, IN2012-1, pp. 1-6, April 2012.
Paper # IN2012-1 
Date of Issue 2012-04-05 (IN) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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reproduction
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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IN  
Conference Date 2012-04-12 - 2012-04-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyotofu-Chusho-Kigyo-Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Home network, Smart Grid, Saving Energy, M2M, Participatory Sensing,Mobile Network, Ubiquitous Ntework, etc 
Paper Information
Registration To IN 
Conference Code 2012-04-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Forecasting individual trends using resolution adaptive arima 
Sub Title (in English)  
Keyword(1) Demand-Prediction  
Keyword(2) Wavelet Transform  
Keyword(3) Time Series Analysis  
Keyword(4) ARIMA Models  
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1st Author's Name Hiroki Nakayama  
1st Author's Affiliation Osaka City University (Osaka City Univ.)
2nd Author's Name Shingo Ata  
2nd Author's Affiliation Osaka City University (Osaka City Univ.)
3rd Author's Name Ikuo Oka  
3rd Author's Affiliation Osaka City University (Osaka City Univ.)
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Speaker
Date Time 2012-04-12 13:30:00 
Presentation Time 25 
Registration for IN 
Paper # IEICE-IN2012-1 
Volume (vol) IEICE-112 
Number (no) no.4 
Page pp.1-6 
#Pages IEICE-6 
Date of Issue IEICE-IN-2012-04-05 


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