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pdc: An R Package for Complexity-Based Clustering of Time Series

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  • Brandmaier, Andreas M.

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.

Suggested Citation

  • Brandmaier, Andreas M., 2015. "pdc: An R Package for Complexity-Based Clustering of Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i05).
    • Handle: RePEc:jss:jstsof:v:067:i05
    DOI: http://hdl.handle.net/10.18637/jss.v067.i05
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    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Mantas Svazas & Valentinas Navickas & Yuriy Bilan & Joanna Nakonieczny & Jana Spankova, 2021. "Biomass Clusterization from a Regional Perspective: The Case of Lithuania," Energies, MDPI, vol. 14(21), pages 1-15, October.
    3. Alireza Farnoush & Ashish Gupta & Hamidreza Ahady Dolarsara & David Paradice & Shashank Rao, 2022. "Going beyond intent to adopt Blockchain: an analytics approach to understand board member and financial health characteristics," Annals of Operations Research, Springer, vol. 308(1), pages 93-123, January.
    4. Martins, Adriel M.F. & Fernandes, Leonardo H.S. & Nascimento, Abraão D.C., 2023. "Scientific progress in information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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