Copula-based fuzzy clustering of spatial time series

- A copula-based clustering algorithm for spatial-time series is suggested.
- The dissimilarity measure combines the dependence and the spatial information.
- The clustering is performed around medoids time series.
- The case study shows the usefulness of the proposed algorithm in regional economics.
- Different simulation studies have been presented.

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.