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| Title: |
Mining for weak periodic signals in time series databases |
| Author(s): |
C. Berberidis, I. Vlahavas.
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| Availability: |
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| Keywords: |
Time Series Mining, Periodicity Detection.
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| Appeared in: |
Intelligent Data Analysis, Dr. F. Famili (Ed.), IOS Press, 9(1), 2005.
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| Abstract: |
Periodicity is a particularly interesting feature, which is often inherent in real world time series data sets. In this article we propose a data mining technique for detecting multiple partial and approximate periodicities. Our approach is exploratory and follows a fil-ter/refine paradigm. In the filter phase we introduce an autocorrelation-based algorithm that produces a set of candidate partial periodicities. The algorithm is extended to capture ap-proximate periodicities. In the refine phase we effectively prune invalid periodicities. We conducted a series of experiments with various real-world data sets to test the performance and verify the quality of the results. |
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