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The aim of this paper is to conduct change point analysis of interval-valued time series employing a regression trees approach. In order to deal with such time series we propose to employ a suitable distance measure that takes into account the underlying structure of interval data. Simulation results pertaining to the behavior of the proposed approach as well as an empirical application on a daily sample of air pollutant are provided, that illustrate the practical usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

Air quality measurement relies on the effectiveness of a network of monitoring stations. Monitoring stations collect information about the evolution of air pollutants concentration. If more stations supplies the same information, then some of them could be deemed as redundant. Then, a clustering model for time series is useful to identify stations with similar features. Time series of pollutant concentration can be classified using the autoregressive metric in the framework of standard clustering techniques. A serious drawback is related to the lack of robustness of standard procedures. In this paper, using a partitioning around medoids approach combined with a trimming-based rule, a fuzzy model for cluster time series is proposed. The model provides a robust alternative to standard procedures. Two simulation studies are carried out to evaluate the clustering performance of the proposed clustering model. Finally, an empirical application to real time series of PM 10 concentration in the Lazio region is presented and discussed showing the practical usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]