Clustering Time Series by Nonlinear Dependence

Michele La Rocca () and Luca Vitale ()
Additional contact information
Michele La Rocca: University of Salerno
Luca Vitale: University of Salerno

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 291-297 from Springer

Abstract: Abstract The problem of time series clustering has attracted growing research interest in the last decade. 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 dependent (possibly nonlinear) structure. We propose a dissimilarity measure based on the auto distance correlation function which is able to detect both linear and nonlinear dependence structures. Once the pairwise dissimilarity matrix for time series has been obtained, a standard clustering algorithm, such as hierarchical clustering algorithm, can be used. Numerical studies based on Monte Carlo experiments show that our method performs reasonably well.

Keywords: Clustering; Nonlinear time series; Autodistance correlation function (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_43

Ordering information: This item can be ordered from
http://www.springer.com/9783030789657

DOI: 10.1007/978-3-030-78965-7_43

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().