Mining for weak periodic signals in time series databases

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.