Cophenetic-based fuzzy clustering of time series by linear dependency Articles uri icon

publication date

  • October 2021

start page

  • 114

end page

  • 136

volume

  • 137

International Standard Serial Number (ISSN)

  • 0888-613X

abstract

  • In this work, a new approach to cluster large sets of time series is presented. The proposed methodology takes into account the dependency among the time series to obtain a fuzzy partition of the set of observations. A two-step procedure to accomplish this is presented. First, the cophenetic distances, based on a time series linear cross-dependency measure, are obtained. Second, these distances are used as an input of a non-Euclidean fuzzy relational clustering algorithm. As a result, we obtain a robust fuzzy procedure capable of detecting groups of time series with different types of cross-dependency. We illustrate the usefulness of the stated methodology through some Monte Carlo experiments and a real data example. Our results show that the methodology proposed in this work substantially improves the hard partitioning clustering alternative.

keywords

  • fuzzy clustering; time series; hierarchical clustering; cophenetic distances