B0254
Title: Spatial weighted robust clustering of multivariate time series with an application to COVID-19 pandemic
Authors: Angel Lopez Oriona - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) [presenting]
Jose Vilar - Universidade da Coruna (Spain)
Pierpaolo Durso - Universita di Roma La Sapienza (Italy)
Abstract: A fuzzy clustering model for multivariate time series based on the quantile cross-spectral density and principal component analysis is improved. The extension consists of (i) a weighting system that assigns a weight to each principal component in accordance with its importance concerning the underlying clustering structure and (ii) a penalization term allowing to take into account the spatial information. The iterative solutions of the new model, which employs the exponential distance in order to gain robustness against outlying series, are derived. A simulation study shows that the introduction of the weighting system substantially enhances the effectiveness of the former approach. The behaviour of the extended model in terms of the spatial penalization term is also analysed. An application involving multivariate time series of mobility indicators concerning COVID-19 pandemic highlights the usefulness of the proposed technique.
