<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Clustering Macroeconomic Time Series

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Clustering Macroeconomic Time Series"}

The data mining technique of time series clustering is well established. However, even when recognized as an unsupervised learning method, it does require...

Clustering Macroeconomic Time Series

Online veröffentlicht: 26. Mai 2018

Seitenbereich: 74 - 88

DOI: https://doi.org/10.15611/eada.2018.2.06

Schlüsselwörter
time series clustering, similarity, cluster analysis, GDP

© 2018 Iwo Augustyński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Clustering Macroeconomic Time Series

Online veröffentlicht: 26. Mai 2018

Seitenbereich: 74 - 88

DOI: https://doi.org/10.15611/eada.2018.2.06

Schlüsselwörtertime series clustering, similarity, cluster analysis, GDP

© 2018 Iwo Augustyński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
time series clustering, similarity, cluster analysis, GDP