Mining Dense Periodic Patterns in Time Series Data

Authors:
Chang Sheng;Wynne Hsu;Mong Li Lee
Affiliations:
National University of Singapore;National University of Singapore;National University of Singapore
Venue:
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Year:
2006

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Abstract

Existing techniques to mine periodic patterns in time series data are focused on discovering full-cycle periodic patterns from an entire time series. However, many useful partial periodic patterns are hidden in long and complex time series data. In this paper, we aim to discover the partial periodicity in local segments of the time series data. We introduce the notion of character density to partition the time series into variable-length fragments and to determine the lower bound of each character's period. We propose a novel algorithm, called DPMiner, to find the dense periodic patterns in time series data. Experimental results on both synthetic and real-life datasets demonstrate that the proposed algorithm is effective and efficient to reveal interesting dense periodic patterns.