Copula-based fuzzy clustering of spatial time series

Authors: Disegna, M., D'Urso, P. and Durante, F.

Journal: Spatial Statistics

Volume: 21

Pages: 209-225

ISSN: 2211-6753

DOI: 10.1016/j.spasta.2017.07.002

Abstract:

This paper contributes to the existing literature on the analysis of spatial time series presenting a new clustering algorithm called COFUST, i.e. COpula-based FUzzy clustering algorithm for Spatial Time series. The underlying idea of this algorithm is to perform a fuzzy Partitioning Around Medoids (PAM) clustering using copula-based approach to interpret comovements of time series. This generalisation allows both to extend usual clustering methods for time series based on Pearson's correlation and to capture the uncertainty that arises assigning units to clusters. Furthermore, its flexibility permits to include directly in the algorithm the spatial information. Our approach is presented and discussed using both simulated and real data, highlighting its main advantages.

http://eprints.bournemouth.ac.uk/29518/

Source: Scopus

Copula-based fuzzy clustering of spatial time series

Authors: Disegna, M., D'Urso, P. and Durante, F.

Journal: Spatial Statistics

Volume: 21

Issue: Part A

Pages: 209-225

Publisher: Elsevier BV

ISSN: 2211-6753

DOI: 10.1016/j.spasta.2017.07.002

Abstract:

This paper contributes to the existing literature on the analysis of spatial time series presenting a new clustering algorithm called COFUST, i.e. COpula-based FUzzy clustering algorithm for Spatial Time series. The underlying idea of this algorithm is to perform a fuzzy Partitioning Around Medoids (PAM) clustering using copula-based approach to interpret comovements of time series. This generalisation allows both to extend usual clustering methods for time series based on Pearson’s correlation and to capture the uncertainty that arises assigning units to clusters. Furthermore, its flexibility permits to include directly in the algorithm the spatial information. Our approach is presented and discussed using both simulated and real data, highlighting its main advantages.

http://eprints.bournemouth.ac.uk/29518/

Source: Manual

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