Publication: Cophenetic-based fuzzy clustering of time series by linear dependency
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2021-10-01
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Elsevier
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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.
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Keywords
Fuzzy clustering, Time series, Hierarchical clustering, Cophenetic distances
Bibliographic citation
Alonso, A. M., D’Urso, P., Bermudo, C., & Guerrero, V. (2021). Cophenetic-based fuzzy clustering of time series by linear dependency. International Journal of Approximate Reasoning, 137, 114-136.