Mining Self-similarity in Time Series

Song Meina, Zhan Xiaosu, Song Junde

2006

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.

References

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Paper Citation


in Harvard Style

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 - Volume 1: CSAC, (ICEIS 2006) ISBN 978-972-8865-53-5, pages 131-136. DOI: 10.5220/0002497501310136


in Bibtex Style

@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 - Volume 1: CSAC, (ICEIS 2006)},
year={2006},
pages={131-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002497501310136},
isbn={978-972-8865-53-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Workshop on Computer Supported Activity Coordination - Volume 1: CSAC, (ICEIS 2006)
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