Robust fuzzy clustering of spatial time series with social economic applications.
The detection of patterns in multivariate time series with ¿contiguity¿ constraints is a relevant task, especially for large datasets. The research aims at proposing clustering models for multivariate time series, with the following characteristics.
First, the Partitioning Around Medoids (PAM) framework is considered.
Among the different approaches to the clustering of multivariate time series, the observation-based is adopted.
To cope with the complexity of the features of each multivariate time series and the associated assignment uncertainty a fuzzy clustering approach is adopted.
To neutralize the effect of possible outliers, the noise, metric and trimmed approach are considered.
The temporal aspect will be dealt with by using appropriate measures of dissimilarity between time trajectories; the contiguity among units by adding a contiguity matrix as a penalization term in the clustering model.
In socio-economic clustering often the empirical information is represented by time-varying data generated by indicators observed over time on a set of territorial units. Usually among these units may exist contiguity relations, spatial but not only. The proposed models are intended to be applied to the classification of the European NUTs on the basis of the observed dynamics of the Basic, Efficiency and Innovation subindexes of the Regional Competitiveness Index (RCI) 2013 and 2016.