Open AccessClustering time series based on dependence structure
Author(s) -
Beibei Zhang,
Baiguo An
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0206753
Subject(s) - cluster analysis , series (stratigraphy) , computer science , pairwise comparison , data mining , estimator , time series , hierarchical clustering , consistency (knowledge bases) , single linkage clustering , correlation clustering , algorithm , mathematics , cure data clustering algorithm , statistics , artificial intelligence , machine learning , paleontology , biology
The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and consider an estimator for it, where the strong consistency of the estimator is guaranteed. Once the pairwise distance matrix for time series has been obtained, we apply a hierarchical clustering algorithm to cluster the time series and ensure its consistency. Numerical studies, including a large number of simulations and analysis of practical data, show that our method performs well.