@misc{Augustyński_Iwo_Clustering_2018,
 author={Augustyński, Iwo and Laskoś-Grabowski, Paweł},
 identifier={DOI: 10.15611/eada.2018.2.06},
 year={2018},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Econometrics = Ekonometria, 2018, Vol. 22, No. 2, s. 74-88},
 language={eng},
 abstract={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},
 title={Clustering macroeconomic time series},
 type={artykuł},
 keywords={time series clustering, similarity, cluster analysis, GDP, grupowanie szeregów czasowych, podobieństwo, analiza skupień, PKB},
}