Discovering patterns in real-valued time series

Authors:
Joe Catalano;Tom Armstrong;Tim Oates
Affiliations:
University of Maryland Baltimore County, Baltimore, MD;University of Maryland Baltimore County, Baltimore, MD;University of Maryland Baltimore County, Baltimore, MD
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

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Abstract

This paper describes an algorithm for discovering variable length patterns in real-valued time series. In contrast to most existing pattern discovery algorithms, ours does not first discretize the data, runs in linear time, and requires constant memory. These properties are obtained by sampling the data stream rather than processing all of the data. Empirical results show that the algorithm performs well on both synthetic and real data when compared to an exhaustive algorithm.