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The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

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Abstract

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.

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Authors and Affiliations

  1. Centro de Investigación en Matemáticas, Guanajuato, México

    Carolina Euán & Joaquín Ortega

  2. King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia

    Carolina Euán & Hernando Ombao

  3. UC Irvine Space-Time Modeling Group, University of California, Irvine, CA, USA

    Carolina Euán & Hernando Ombao

  4. Department of Statistics, University of California, Irvine, Irvine, CA, USA

    Hernando Ombao

Authors
  1. Carolina Euán
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  2. Hernando Ombao
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  3. Joaquín Ortega
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Correspondence to Carolina Euán.

Additional information

The authors would like to thank the reviewers for their comments which led to improvements in this work.

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Euán, C., Ombao, H. & Ortega, J. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure. J Classif 35, 71–99 (2018). https://doi.org/10.1007/s00357-018-9250-5

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  • DOI: https://doi.org/10.1007/s00357-018-9250-5

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