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Efficient Periodicity Mining in Time Series Databases Using Suffix Trees
January 2011 (vol. 23 no. 1)
pp. 79-94
ASCII Text
x 
 
Faraz Rasheed, Mohammed Alshalalfa, Reda Alhajj, "Efficient Periodicity Mining in Time Series Databases Using Suffix Trees," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 79-94, January, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.76,
author = {Faraz Rasheed and Mohammed Alshalalfa and Reda Alhajj},
title = {Efficient Periodicity Mining in Time Series Databases Using Suffix Trees},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {1},
issn = {1041-4347},
year = {2011},
pages = {79-94},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.76},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Efficient Periodicity Mining in Time Series Databases Using Suffix Trees
IS - 1
SN - 1041-4347
SP79
EP94
EPD - 79-94
A1 - Faraz Rasheed,
A1 - Mohammed Alshalalfa,
A1 - Reda Alhajj,
PY - 2011
KW - Time series
KW - periodicity detection
KW - suffix tree
KW - symbol periodicity
KW - segment periodicity
KW - sequence periodicity
KW - noise resilient.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Faraz Rasheed, University of Calgary, Calgary
Mohammed Alshalalfa, University of Calgary, Calgary
Reda Alhajj, University of Calgary, Calgary
Periodic pattern mining or periodicity detection has a number of applications, such as prediction, forecasting, detection of unusual activities, etc. The problem is not trivial because the data to be analyzed are mostly noisy and different periodicity types (namely symbol, sequence, and segment) are to be investigated. Accordingly, we argue that there is a need for a comprehensive approach capable of analyzing the whole time series or in a subsection of it to effectively handle different types of noise (to a certain degree) and at the same time is able to detect different types of periodic patterns; combining these under one umbrella is by itself a challenge. In this paper, we present an algorithm which can detect symbol, sequence (partial), and segment (full cycle) periodicity in time series. The algorithm uses suffix tree as the underlying data structure; this allows us to design the algorithm such that its worst-case complexity is O(k . n^2), where k is the maximum length of periodic pattern and n is the length of the analyzed portion (whole or subsection) of the time series. The algorithm is noise resilient; it has been successfully demonstrated to work with replacement, insertion, deletion, or a mixture of these types of noise. We have tested the proposed algorithm on both synthetic and real data from different domains, including protein sequences. The conducted comparative study demonstrate the applicability and effectiveness of the proposed algorithm; it is generally more time-efficient and noise-resilient than existing algorithms.

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Index Terms:
Time series, periodicity detection, suffix tree, symbol periodicity, segment periodicity, sequence periodicity, noise resilient.
Citation:
Faraz Rasheed, Mohammed Alshalalfa, Reda Alhajj, "Efficient Periodicity Mining in Time Series Databases Using Suffix Trees," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 79-94, Jan. 2011, doi:10.1109/TKDE.2010.76
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