Tail-dependence clustering of time series with spatial constraints

We introduce a clustering method for time series based on tail dependence. Such a method also considers spatial constraints by means of a suitable procedure merging temporal and spatial dependence via extreme-value copulas. The cluster composition depends on the choice of the hyper-parameter \(\alpha \in (0,1)\) $\alpha \in (0,1)$ used to calibrate the contribution of the spatial dependence to the overall dissimilarity. A novel heuristic approach to select \(\alpha\) $\alpha$ based on a suitable connectedness index associated to each cluster of the  partition is proposed.