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

Carolina Euán*, Hernando Ombao, Joaquín Ortega

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)71-99
Number of pages29
JournalJournal of Classification
Issue number1
StatePublished - Apr 1 2018


  • Hierarchical clustering
  • Hierarchical spectral merger clustering: Time series clustering
  • Spectral analysis
  • Time series
  • Total variation distance

ASJC Scopus subject areas

  • Library and Information Sciences
  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty


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