Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Andreas M. Brandmaier
Title: pdc: An R Package for Complexity-Based Clustering of Time Series
Abstract: Permutation distribution clustering is a complexity-based approach to clustering time series. The dissimilarity of time series is formalized as the squared Hellinger distance between the permutation distribution of embedded time series. The resulting distance measure has linear time complexity, is invariant to phase and monotonic transformations, and robust to outliers. A probabilistic interpretation allows the determination of the number of significantly different clusters. An entropy-based heuristic relieves the user of the need to choose the parameters of the underlying time-delayed embedding manually and, thus, makes it possible to regard the approach as parameter-free. This approach is illustrated with examples on empirical data.

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Paper: pdc: An R Package for Complexity-Based Clustering of Time Series     Download PDF (Downloads: 2561)
pdc_1.0.3.tar.gz: R source package Download (Downloads: 116; 115KB)
v67i05.R: R replication code Download (Downloads: 121; 3KB)

DOI: 10.18637/jss.v067.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.