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Decomposing the global financial crisis: A Self-Organizing Time Map
•This paper applies the Self-Organizing Time Map (SOTM) in financial stability surveillance.•The SOTM is combined with classical cluster analysis for visual dynamic clustering.•The SOTM presents a holistic view of cross-sectional patterns over time.•The SOTM is used for identifying temporal structur...
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Published in: | Pattern recognition letters 2013-10, Vol.34 (14), p.1701-1709 |
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Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •This paper applies the Self-Organizing Time Map (SOTM) in financial stability surveillance.•The SOTM is combined with classical cluster analysis for visual dynamic clustering.•The SOTM presents a holistic view of cross-sectional patterns over time.•The SOTM is used for identifying temporal structural changes.•The SOTM is used for decomposing the global financial crisis that started in 2007.
A key starting point for financial stability surveillance is understanding past, current and possible future risks and vulnerabilities. Through temporal data and dimensionality reduction, or visual dynamic clustering, this paper aims to present a holistic view of cross-sectional macro-financial patterns over time. The Self-Organizing Time Map (SOTM) is a recent adaptation of the Self-Organizing Map for exploratory temporal structure analysis, which disentangles cross-sectional data structures over time. We apply the SOTM, as well as its combination with classical cluster analysis, in financial stability surveillance. Thus, this paper uses the SOTM for decomposing and identifying temporal structural changes in macro-financial data before, during and after the global financial crisis of 2007–2009. |
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ISSN: | 0167-8655 1872-7344 |
DOI: | 10.1016/j.patrec.2013.03.017 |