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Extreme-based Clustering of Environmental Time Series

Manuel G. Scotto
Departamento de Matemática
Universidade de Aveiro, Portugal
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Susana M. Barbosa
Instituto Dom Luiz
Universidade de Lisboa, Portugal
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Andrés M. Alonso
Departamento de Estadística
Instituto Flores de Lemus
Universidad Carlos III de Madrid, España
  • Abstract
    This work provides an up-to-date review on clustering techniques to classify time series on the basis of their corresponding extremal properties with a bias towards describing the authors’ ongoing work. Applications to clustering time series of sea-level and daily mean temperature are presented.
  • Keywords: Extreme value theory, Cluster analysis, Bayesian analysis, Return values.
  • AMS Subject classifications: 62H30, 62C10, 60G70.