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Using Permutations for Hierarchical Clustering of Time Series

Departamento de Matemática Aplicada y Estadística, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
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Entropy 2019, 21(3), 306; https://doi.org/10.3390/e21030306
Received: 5 February 2019 / Revised: 8 March 2019 / Accepted: 17 March 2019 / Published: 21 March 2019
Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method. View Full-Text
Keywords: time series clustering; permutation entropy; time series dependency; hierarchical clustering; mutual information time series clustering; permutation entropy; time series dependency; hierarchical clustering; mutual information
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Cánovas, J.S.; Guillamón, A.; Ruiz-Abellón, M.C. Using Permutations for Hierarchical Clustering of Time Series. Entropy 2019, 21, 306.

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