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 and 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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IN2012-1 |
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) |
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Keyword(1) |
Demand-Prediction |
Keyword(2) |
Wavelet Transform |
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Time Series Analysis |
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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 |
Author-1 |
Date Time |
2012-04-12 13:30:00 |
Presentation Time |
25 minutes |
Registration for |
IN |
Paper # |
IN2012-1 |
Volume (vol) |
vol.112 |
Number (no) |
no.4 |
Page |
pp.1-6 |
#Pages |
6 |
Date of Issue |
2012-04-05 (IN) |
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