  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.
