Discovering patterns in categorical time series using IFS

Authors:
Christian H. Weií;Rainer Göb
Affiliations:
University of Würzburg, Institute of Mathematics, Department of Statistics, Am Hubland, D-97074 Würzburg, Germany;University of Würzburg, Institute of Mathematics, Department of Statistics, Am Hubland, D-97074 Würzburg, Germany
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

The detection of patterns in categorical time series data is an important task in many fields of science. Several efficient algorithms for finding frequent sequential patterns have been proposed. An online-approach for sequential pattern analysis based on transforming the categorical alphabet to real vectors and generating fractals by an iterated function systems (IFS) is suggested. Sequential patterns can be analyzed with standard methods of cluster analysis using this approach. A version of the procedure allows detecting patterns visually.