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Title: Multiple and Partial Periodicity Mining in Time Series Databases
Author(s): C. Berberidis, A. Walid, M. Atallah, I. Vlahavas, A. Elmagarmid.
Availability: Click here to download the PDF (Acrobat Reader) file (4 pages).
Appeared in: Proc. 15th European Conference on Artificial Intelligence (ECAI '02), Frank Van Harmelen (Ed.), IOS Press, pp. 370-374, Lyon, France, 2002.
Abstract: Periodicity search in time series is a problem that has been investigated by mathematicians in various areas, such as statistics, economics, and digital signal processing. For large databases of time series data, scalability becomes an issue that traditional techniques fail to address. In existing time series mining algorithms for detecting periodic patterns, the period length is user-specified. This is a drawback especially for datasets where no period length is known in advance. We propose an algorithm that extracts a set of candidate periods featured in a time series that satisfy a minimum confidence threshold, by utilizing the autocorrelation function and FFT as a filter. We provide some mathematical background as well as experimental results.
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        This paper has been cited by the following:

1 Sheng Chang, Hsu Wynne, Lee Mong Li, Efficient Mining of Dense Periodic Patterns in Time Series, Technical report, Scool of Computing, National University of Singapore, TR20/05, URI: http://hdl.handle.net/1900.100/1859, October 2005.
2 Srini Parthasarathy, Sameep Mehta, and Soundararajan Srinivasan, "Robust Periodicity Detection Algorithms," Electronic report OSU-CISRC-3/06--TR29, Technical Report Series, Computer Science and Engineering Research Cen-ter, Department of Computer Science and Engineering, The Ohio State Uni-versity, 2006.
3 Sheng Chang, Hsu Wynne, Lee Mong Li, Lee, “Mining Dense Periodic Pat-terns in Time Series Data”, Proc. of the 22nd International Conference on Data Engineering (ICDE'06), IEEE Computer Society, Washington, 2006.
4 Zhen He, X. Sean Wang, Byung Suk Lee and Alan C. H. Ling, Mining partial periodic correlations in time series, Knowledge and Information Systems, Springer, 15(1), April 2008.