STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

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.

[1]  Walid G. Aref,et al.  Periodicity detection in time series databases , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  A. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[3]  Jignesh M. Patel,et al.  Practical methods for constructing suffix trees , 2005, The VLDB Journal.

[4]  김동규,et al.  [서평]「Algorithms on Strings, Trees, and Sequences」 , 2000 .

[5]  Philip S. Yu,et al.  InfoMiner+: mining partial periodic patterns with gap penalties , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[6]  Hongjun Lu,et al.  Constructing suffix tree for gigabyte sequences with megabyte memory , 2005, IEEE Transactions on Knowledge and Data Engineering.

[7]  Ronald K. Pearson,et al.  BMC Bioinformatics BioMed Central Methodology article , 2005 .

[8]  Christos Faloutsos,et al.  AWSOM: Adaptive, Hands-Off Stream Mining , 2003 .

[9]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[10]  Walid G. Aref,et al.  Multiple and Partial Periodicity Mining in Time Series Databases , 2002, ECAI.

[11]  Jie Chen,et al.  Bioinformatics Original Paper Detecting Periodic Patterns in Unevenly Spaced Gene Expression Time Series Using Lomb–scargle Periodograms , 2022 .

[12]  Joseph L. Hellerstein,et al.  Mining partially periodic event patterns with unknown periods , 2001, Proceedings 17th International Conference on Data Engineering.

[13]  Azzedine Lansari,et al.  A new non-recursive algorithm for binary search tree traversal , 2003, 10th IEEE International Conference on Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003.

[14]  Christos Faloutsos,et al.  Adaptive, Hands-Off Stream Mining , 2003, VLDB.

[15]  Zvi Galil,et al.  Faster tree pattern matching , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[16]  Roberto Grossi,et al.  Suffix trees and their applications in string algorithms , 1993 .

[17]  Walid G. Aref,et al.  WARP: time warping for periodicity detection , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[18]  Esko Ukkonen,et al.  On-line construction of suffix trees , 1995, Algorithmica.

[19]  Mohammed Al-Shalalfa,et al.  Adapting Machine Learning Technique for Periodicity Detection in Nucleosomal Locations in Sequences , 2007, IDEAL.

[20]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[21]  Piotr Indyk,et al.  Identifying Representative Trends in Massive Time Series Data Sets Using Sketches , 2000, VLDB.

[22]  Gregory Kucherov,et al.  Finding maximal repetitions in a word in linear time , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[23]  Jianyong Wang,et al.  Mining Complex Time-Series Data by Learning Markovian Models , 2006, Sixth International Conference on Data Mining (ICDM'06).