PL EN


Journal
2018 | vol. 22 no. 2 | 74-88
Article title

Clustering macroeconomic time series

Content
Title variants
PL
Grupowanie makroekonomicznych szeregów czasowych
Languages of publication
EN
Abstracts
EN
The data mining technique of time series clustering is well established. However, even when recognized as an unsupervised learning method, it does require making several design decisions that are nontrivially influenced by the nature of the data involved. By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena, such as crises. We also successfully apply our findings to the analysis of national economies, specifically to identifying their structural relations.
Journal
Year
Pages
74-88
Physical description
Contributors
References
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-01e212e5-c841-4ca2-900a-c5981b75be55
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.