An Improved Framework for Outlier Periodic Pattern Detection in Time Series
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014

An Improved Framework for Outlier Periodic Pattern Detection in Time Series

Sulochana Gagare-Kadam

Periodic pattern detection in time-series is an important data mining task. Detecting the periodicity of outlier patterns might be more important in many sequences than the periodicity of regular, more frequent patterns. Patterns which repeat over a period of time are known as periodic patterns. Outlier Pattern are those which occur unusually or surprisingly. In this paper, I present the development of an enhanced suffix tree-based algorithm capable of detecting the periodicity of outlier patterns in a time series using MAD (Median Absolute Deviation) is presented. An existing algorithm makes use of mean values, which is inefficient. Use of MAD increases the output of these algorithms and gives more accurate information.

Keywords: Periodic, pattern, data mining,

Edition: Volume 3 Issue 12, December 2014

Pages: 826 - 830

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How to Cite this Article?

Sulochana Gagare-Kadam, "An Improved Framework for Outlier Periodic Pattern Detection in Time Series", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB14407, Volume 3 Issue 12, December 2014, 826 - 830

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