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Authors: Song Meina ; Zhan Xiaosu and Song Junde

Affiliation: Beijing University of Posts and Telecommunications, China

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Self-similarity can successfully characterize and forecast intricate, non-periodic and chaos time series avoiding the limitation of traditional methods on LRD (Long-Range Dependence). The potential principals will be found and the future unknown time series will be forecasted through foregoing training. Therefore it is important to mine the LRD by self-similarity analysis. In this paper, mining self-similarity of time series is introduced. And the practical value can be found from two cases study respectively for season- variable trend forecast and network traffic.

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Paper citation in several formats:
Meina, S.; Xiaosu, Z. and Junde, S. (2006). Mining Self-similarity in Time Series. In Proceedings of the 3rd International Workshop on Computer Supported Activity Coordination (ICEIS 2006) - CSAC; ISBN 978-972-8865-53-5, SciTePress, pages 131-136. DOI: 10.5220/0002497501310136

@conference{csac06,
author={Song Meina. and Zhan Xiaosu. and Song Junde.},
title={Mining Self-similarity in Time Series},
booktitle={Proceedings of the 3rd International Workshop on Computer Supported Activity Coordination (ICEIS 2006) - CSAC},
year={2006},
pages={131-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002497501310136},
isbn={978-972-8865-53-5},
}

TY - CONF

JO - Proceedings of the 3rd International Workshop on Computer Supported Activity Coordination (ICEIS 2006) - CSAC
TI - Mining Self-similarity in Time Series
SN - 978-972-8865-53-5
AU - Meina, S.
AU - Xiaosu, Z.
AU - Junde, S.
PY - 2006
SP - 131
EP - 136
DO - 10.5220/0002497501310136
PB - SciTePress