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Decomposing the Global Financial Crisis: A Self-Organizing Time Map

Peter Sarlin, Decomposing the Global Financial Crisis: A Self-Organizing Time Map. In: Manuel Graña, Carlos Toro, Jorge Posada, Robert Howlett, Lakhmi Jain (Eds.), Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'12), 798–806, IOS Press, 2012.

Abstract:

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 presents a holistic view of cross-sectional patterns over time with no explicit dependence on historical data. We propose using in financial stability surveillance the Self-Organizing Time Map (SOTM), a recent adaptation of the Self-Organizing Map for exploratory temporal structure analysis, which disentangles cross-sectional data structures over time. 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.

BibTeX entry:

@INPROCEEDINGS{inpSarlin_Peter12b,
  title = {Decomposing the Global Financial Crisis: A Self-Organizing Time Map},
  booktitle = {Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'12)},
  author = {Sarlin, Peter},
  editor = {Graña, Manuel and Toro, Carlos and Posada, Jorge and Howlett, Robert and Jain, Lakhmi},
  publisher = {IOS Press},
  pages = {798–806},
  year = {2012},
  keywords = {financial crisis, Self-Organizing Time Map, exploratory temporal structure analysis, structural changes, financial stability surveillance},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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