Abstract
Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise.
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References
Indyk P, Koudas N, Muthukrishnan S (2000) Identifying representative trends in massive time series data sets using sketches. In: Proceedings of the international conference on very large data bases, Sept 2000
Elfeky MG, Aref WG, Elmagarmid AK (2005) Periodicity detection in time series databases. IEEE Trans Knowl Data Eng 17(7):875–887
Gusfield D (1997) Algorithms on strings, trees, and sequences. Cambridge University Press, Cambridge
Weigend A, Gershenfeld N (1994) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading
Ukkonen E (1995) Online construction of suffix trees. Algorithmica 14(3):249–260
Ma S, Hellerstein J (2001) Mining partially periodic event patterns with unknown periods. In: Proceedings of IEEE international conference on data engineering, Apr 2001
Yang J, Wang W, Yu P (2002) InfoMiner+: Mining partial periodic patterns with gap penalties. In: Proceedings of IEEE international conference on data mining, Dec 2002
Berberidis C, Aref W, Atallah M, Vlahavas I, Elmagarmid A (2002) Multiple and partial periodicity mining in time series databases. In: Proceedings of the European conf, artificial intelligence, Jul 2002
Grossi R, Italiano GF (1993) Suffix trees and their applications in string algorithms. In: Proceedings of South American workshop on string processing, Sep 1993, pp 57–76
Dubiner M et al. (1994) Faster tree pattern matching. J Assoc Comput Mach 14:205–213
Kolpakov R, Kucherov G (1999) Finding maximal repetitions in a word in linear time. In: Proceedings of the annual symposium on foundations of computer science, pp 596–604
Al-Rawi A, Lansari A, Bouslama F (2003) A new non-recursive algorithm for binary search tree traversal. In: Proceedings of IEEE international conference on electronics, circuits and systems, vol 2, pp 770–773, UAE, Dec 2003
Elfeky MG, Aref WG, Elmagarmid AK (2005) WARP: time warping for periodicity detection. In: Proceedings of IEEE international conference on data mining, pp 138–145
Papadimitriou S, Brockwell A, Faloutsos C (2003) Adaptive, hands off-stream mining. In: Proceedings of the international conference on very large databases
Rasheed F, Alshalalfa M, Alhajj R (2007) Adapting machine learning technique for periodicity detection in nucleosomal locations in sequences. In: Proceedings of the international conference on intelligent data engineering and automated learning, IDEAL’07, Dec 2007, Birmingham, UK. LNCS series. Springer, Berlin
Wang Y, Zhou L, Feng J, Wang J, Liu Z-Q (2006) Mining complex time-series data by learning Markovian models. In: Proceedings of IEEE international conference on data mining, pp 1136–1140
Ahdesmäki M, Lähdesmäki H, Pearson R, Huttunen H, Yli-Harja O (2005) Robust detection of periodic time series measured from biological systems. BMC Bioinformatics 6:117
Glynn EF, Chen J, Mushegian AR (2006) Detecting periodic patterns in unevenly spaced gene expression time series using Lomb–Scargle periodograms. Bioinformatics 22(3):310–316
Cheung C-F, Yu JX, Lu H (2005) Constructing suffix tree for gigabyte sequences with megabyte memory. IEEE Trans Knowl Data Eng 17(1):90–105
Tian Y, Tata S, Hankins RA, Patel JM (2005) Practical methods for constructing suffix trees. VLDB J 14(3):281–299
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Rasheed, F., Alhajj, R. STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases. Appl Intell 32, 267–278 (2010). https://doi.org/10.1007/s10489-008-0144-9
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DOI: https://doi.org/10.1007/s10489-008-0144-9