C. Berberidis, I. Vlahavas, “Mining for weak periodic signals in time series databases”, Intelligent Data Analysis, Dr. F. Famili (Ed.), IOS Press, 9(1), 2005.
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.
