Semi-supervised time-series clustering using Gaussian copulas

The clustering of geo-referenced time series observed across different locations may be enhanced by incorporating spatial information to improve the interpretability of the resulting partitions. To achieve this, it is possible to define a suitable dissimilarity measure that accounts for spatial constraints. This paper explores three distinct approaches to integrating temporal and spatial dependencies within a Gaussian copula-based framework. The proposed methodologies are demonstrated through a case study involving climatological data.