Discovering patterns in categorical time series using IFS

Christian H. Weiß and Rainer Göb

Computational Statistics & Data Analysis, 2008, vol. 52, issue 9, 4369-4379

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.

Date: 2008
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