Using permutations for hierarchical clustering of time series
Área de conocimiento
Estadística e Investigación Operativa; Matemática AplicadaPatrocinadores
Authors have been partially supported by the Grant MTM2017-84079-P from Agencia Estatal de Investigacion (AEI) y Fondo Europeo de Desarrollo Regional (FEDER) and the Ministerio de Economia, Industria y Competitividad (Agencia Estatal de Investigacion, Spanish Government) under research project ENE-2016-78509-C3-2-P, and EU FEDER funds.Realizado en/con
Universidad Politécnica de CartagenaFecha de publicación
2019-03-21Editorial
MDPICita bibliográfica
Cánovas JS, Guillamón A, Ruiz-Abellón MC. Using Permutations for Hierarchical Clustering of Time Series. Entropy. 2019; 21(3):306. https://doi.org/10.3390/e21030306Revisión por pares
SiPalabras clave
Time series clusteringPermutation entropy
Time series dependency
Hierarchical clustering
Mutual information
Resumen
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.
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