Using Permutations for Hierarchical Clustering of Time Series

Entropy

JoseS. Cánovas¹  MaríaCarmen Ruiz-Abellón¹  Antonio Guillamón¹ 

[1] Departamento de Matemática Aplicada y Estadística, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;

关键词: time series clustering;    permutation entropy;    time series dependency;    hierarchical clustering;    mutual information;   

DOI  :  10.3390/e21030306

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown