Clustering of interval time series

Elizabeth Ann Maharaj, Paulo Teles, Paula Brito

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


Interval time series occur when real intervals of some variable of interest are registered as an ordered sequence along time. We address the problem of clustering interval time series (ITS), for which different approaches are proposed. First, clustering is performed based on point-to-point comparisons. Time-domain and wavelet features also serve as clustering variables in alternative approaches. Furthermore, autocorrelation matrix functions, gathering the autocorrelation and cross-correlation functions of the ITS upper and lower bounds, may be compared using adequate distances (e.g. the Frobenius distance) and used for clustering ITS. An improved procedure to determine the autocorrelation function of ITS is proposed, which also serves as a basis for clustering. The different alternative approaches are explored and their performances compared for ITS simulated under different setups. An application to sea level daily ranges, observed at different locations in Australia, illustrates the proposed methods.

Original languageEnglish
Pages (from-to)1011-1034
Number of pages24
JournalStatistics and Computing
Issue number5
Publication statusPublished - Sep 2019


  • Interval autocorrelation
  • Interval data
  • Interval time series
  • Time series clustering

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