Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/180670 
Erscheinungsjahr: 
2018
Quellenangabe: 
[Journal:] ECONOMETRICS. EKONOMETRIA: Advances in Applied Data Analysis [ISSN:] 2449-9994 [Volume:] 22 [Issue:] 2 [Publisher:] Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu [Place:] Wrocław [Year:] 2018 [Pages:] 74-88
Verlag: 
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław
Zusammenfassung: 
The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series clustering method for macroeconomics research, and to develop the most suitable methodology. 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 analysis of national economies, specifically to identifying their structural relations.
Schlagwörter: 
time series clustering
similarity
cluster analysis
GDP
JEL: 
E00
C18
C63
DOI der veröffentlichten Version: 
Creative-Commons-Lizenz: 
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Dokumentart: 
Article
Dokumentversion: 
Accepted Manuscript (Postprint)
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