research-article

Periodicity Detection in Time Series Databases

Authors:

Mohamed G. Elfeky,

Ahmed K. ElmagarmidAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 17, Issue 7

Pages 875 - 887

https://doi.org/10.1109/TKDE.2005.114

Published: 01 July 2005 Publication History

Abstract

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n\log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.

References

[1]

K. Abrahamson, “Generalized String Matching,” SIAM J. Computing, vol. 16, no. 6, pp. 1039-1051, 1987.

Digital Library

[2]

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int'l Conf. Very Large Data Bases, Sept. 1994.

Digital Library

[3]

R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. 11th Int'l Conf. Data Eng., Mar. 1995.

Digital Library

[4]

W. Aref M. Elfeky and A. Elmagarmid, “Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 3, pp.nbsp332-342, Mar. 2004.

Digital Library

[5]

M. Atallah F. Chyzak and P. Dumas, “A Randomized Algorithm for Approximate String Matching,” Algorithmica, vol. 29, no. 3, pp.nbsp468-486, 2001.

Digital Library

[6]

J. Ayres J. Gehrke T. Yiu and J. Flannick, “Sequential Pattern Mining Using a Bitmap Representation,” Proc. Eighth Int'l Conf. Knowledge Discovery and Data Mining, July 2002.

Digital Library

[7]

C. Berberidis W. Aref M. Atallah I. Vlahavas and A. Elmagarmid, “Multiple and Partial Periodicity Mining in Time Series Databases,” Proc. 15th European Conf. Artificial Intelligence, July 2002.

Digital Library

[8]

C. Bettini X. Wang S. Jajodia and J. Lin, “Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences,” IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, pp. 222-237, Mar./Apr. 1998.

Digital Library

[9]

T. Cormen C. Leiserson and R. Rivest, Introduction to Algorithms. Cambridge, Mass.: The MIT Press, 1990.

Digital Library

[10]

C. Daw C. Finney and E. Tracy, “A Review of Symbolic Analysis of Experimental Data,” Rev. Scientific Instruments, vol. 74, no. 2, pp.nbsp915-930, 2003.

[11]

M. Elfeky W. Aref and A. Elmagarmid, “Using Convolution to Mine Obscure Periodic Patterns in One Pass,” Proc. Ninth Int'l Conf. Extending Data Base Technology, Mar. 2004.

[12]

M. Garofalakis R. Rastogi and K. Shim, “SPIRIT: Sequential Pattern Mining with Regular Expression Constraints,” Proc. 25th Int'l Conf. Very Large Data Bases, Sept. 1999.

Digital Library

[13]

J. Han G. Dong and Y. Yin, “Efficient Mining of Partial Periodic Patterns in Time Series Databases,” Proc. 15th Int'l Conf. Data Eng., Mar. 1999.

Digital Library

[14]

J. Han W. Gong and Y. Yin, “Mining Segment-Wise Periodic Patterns in Time Related Databases,” Proc. Fourth Int'l Conf. Knowledge Discovery and Data Mining, Aug. 1998.

Digital Library

[15]

P. Indyk N. Koudas and S. Muthukrishnan, “Identifying Representative Trends in Massive Time Series Data Sets Using Sketches,” Proc. 26th Int'l Conf. Very Large Data Bases, Sept. 2000.

Digital Library

[16]

E. Keogh S. Chu D. Hart and M. Pazzani, “Segmenting Time Series: A Survey and Novel Approach,” Data Mining in Time Series Databases, M. Last, A. Kandel, and H. Bunke, eds., World Scientific Publishing, June 2004.

[17]

E. Keogh S. Lonardi and B. Chiu, “Finding Surprising Patterns in a Time Series Database in Linear Time and Space,” Proc. Eighth Int'l Conf. Knowledge Discovery and Data Mining, July 2002.

Digital Library

[18]

D. Knuth, The Art of Computer Programming, vol. 2, second ed., series in computer science and information processing, Reading, Mass.: Addison-Wesley, 1981.

Digital Library

[19]

S. Ma and J. Hellerstein, “Mining Partially Periodic Event Patterns with Unknown Periods,” Proc. 17th Int'l Conf. Data Eng., Apr. 2001.

Digital Library

[20]

B. Ozden S. Ramaswamy and A. Silberschatz, “Cyclic Association Rules,” Proc. 14th Int'l Conf. Data Eng., Feb. 1998.

Digital Library

[21]

R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proc. Fifth Int'l Conf. Extending Data Base Technology, Mar. 1996.

Digital Library

[22]

J. Vitter, “External Memory Algorithms and Data Structures: Dealing with Massive Data,” ACM Computing Surveys, vol. 33,no. 2, pp. 209-271, June 2001.

Digital Library

[23]

A. Weigend and N. Gershenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past. Reading, Mass.: Addison-Wesley, Reading, 1994.

[24]

J. Yang W. Wang and P. Yu, “Mining Asynchronous Periodic Patterns in Time Series Data,” Proc. Sixth Int'l Conf. Knowledge Discovery and Data Mining, Aug. 2000.

Digital Library

[25]

J. Yang W. Wang and P. Yu, “InfoMiner +: Mining Partial Periodic Patterns with Gap Penalties,” Proc. Second Int'l Conf. Data Mining, Dec. 2002.

Digital Library

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Periodicity Detection in Time Series Databases

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Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 17, Issue 7

July 2005

145 pages

ISSN:1041-4347

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Copyright © 2005.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 July 2005

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Cited By

Zhang YDong HNottingham ABuchanan MBrown DSun Y(2023)Global Analysis with Aggregation-based Beaconing Detection across Large Campus NetworksProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627126(565-579)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627126
Ermshaus ASchäfer PLeser U(2023)ClaSP: parameter-free time series segmentationData Mining and Knowledge Discovery10.1007/s10618-023-00923-x37:3(1262-1300)Online publication date: 15-Feb-2023
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He SQu Z(2019)Research on the Periodical Behavior Discovery of Funds in Anti-money Laundering InvestigationProceedings of the 2019 11th International Conference on Machine Learning and Computing10.1145/3318299.3318356(516-520)Online publication date: 22-Feb-2019
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