Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.

Robust DTW-based entropy fuzzy clustering of time series / D'Urso, Pierpaolo; De Giovanni, Livia; Vitale, Vincenzina. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (In corso di stampa), pp. 1-35. [10.1007/s10479-023-05720-9]

Robust DTW-based entropy fuzzy clustering of time series

D'Urso P.;De Giovanni L.;Vitale V.
In corso di stampa

Abstract

Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.
In corso di stampa
Robust fuzzy C-medoids method Entropy Exponential transformation Three-way data Outliers
Robust DTW-based entropy fuzzy clustering of time series / D'Urso, Pierpaolo; De Giovanni, Livia; Vitale, Vincenzina. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (In corso di stampa), pp. 1-35. [10.1007/s10479-023-05720-9]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/236685
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