Clustering macroeconomic time series

• Authors: Iwo Augustyński , Paweł Laskoś-Grabowski

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.

Keywords

EN

time series clustering; similarity; cluster analysis; GDP

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2018
• Issue: vol. 22 no. 2
• Pages: 74-88

Contributors

author

Iwo Augustyński

author

Paweł Laskoś-Grabowski