Fuzzy clustering of the self-organizing map: some applications on financial time series

Authors:
Peter Sarlin;Tomas Eklund
Affiliations:
Turku Centre for Computer Science - TUCS, Department of Information Technologies, Åbo Akademi University, Turku, Finland and European Central Bank, Kaiserstrasse, Frankfurt am Main, Germany;Turku Centre for Computer Science - TUCS, Department of Information Technologies, Åbo Akademi University, Turku, Finland
Venue:
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Year:
2011

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Abstract

The Self-organizing map (SOM) has been widely used in financial applications, not least for time-series analysis. The SOM has not only been utilized as a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particular cluster is not always easy to judge. To this end, we propose a fuzzy C-means clustering of the units of two previously presented SOM models for financial time-series analysis: financial benchmarking of companies and monitoring indicators of currency crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.