Temporal clustering of time series via threshold autoregressive models: application to commodity prices

Sipan Aslan (), Ceylan Yozgatligil and Cem Iyigun
Additional contact information
Sipan Aslan: Middle East Technical University
Ceylan Yozgatligil: Middle East Technical University
Cem Iyigun: Middle East Technical University

Annals of Operations Research, 2018, vol. 260, issue 1, No 4, 77 pages

Abstract: Abstract The primary aim in this study is grouping time series according to the similarity between their data generating mechanisms (DGMs) rather than comparing pattern similarities in the time series trajectories. The approximation to the DGM of each series is accomplished by fitting the linear autoregressive and the non-linear threshold autoregressive models, and outputs of the estimates are used for feature extraction. Threshold autoregressive models are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. The proposed clustering approach is mainly based on feature vectors derived from above-mentioned models estimates. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time. The efficiency of the proposed approach is demonstrated through a simulation study and the results are compared with other proposed time series clustering methods. An illustration of the proposed clustering approach is given by application to several commodity prices. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about temporal behavior and the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. Furthermore, generating a time varying commodity prices index and sub-indexes can become possible. Findings suggested that clusters of the prices series have been affected with the global financial crisis in 2008 and the data generating mechanisms of prices and so the clusters of prices might not be the same across the entire time-period of the analysis.

Keywords: Time series clustering; Spectral clustering; Commodity prices (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2659-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:annopr:v:260:y:2018:i:1:d:10.1007_s10479-017-2659-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-017-2659-0

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().