ECAI-2002 Conference Paper

[full paper]

Multiple and Partial Periodicity Mining in Time Series Databases

Christos Berberidis, Walid G. Aref, Mikhail Atallah, Ioannis Vlahavas, Ahmed K. Elmagarmid

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.

Keywords: Data Mining and Knowledge Discovery

Citation: Christos Berberidis, Walid G. Aref, Mikhail Atallah, Ioannis Vlahavas, Ahmed K. Elmagarmid: Multiple and Partial Periodicity Mining in Time Series Databases . In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.370-374.