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Presenter : CHANG, SHIH-JIE Authors : Peter Sarlin * 2013.PRL

Decomposing the global financial crisis: A Self-Organizing Time Map. Presenter : CHANG, SHIH-JIE Authors : Peter Sarlin * 2013.PRL. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : CHANG, SHIH-JIE Authors : Peter Sarlin * 2013.PRL

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  1. Decomposing the global financial crisis: A Self-Organizing Time Map Presenter : CHANG, SHIH-JIE Authors : Peter Sarlin*2013.PRL

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation  Over the past years, modeling has oftentimes been attempted through early-warning models relying on conventional statistical methods and historical data.  Key challenge for early-warning modeling is the changing nature of crises due to, e.g.financial innovation.

  4. Objectives • The SOTM performs an abstraction of temporal and cross-sectional patterns through data and dimensionality reduction. The approach differs from traditional static exploratory analyses in that the SOTM dynamically adapts to structural changes in cross-sectional data over time, as well as visualizes the evolution of cluster structures.

  5. Methodology 1 . . M

  6. Methodology 1 . . M

  7. Methodology K l 100*900 . 10 =90*10=900 Silhouette coefficient 100+900 100*150 . 10 =60*10=600 100+150

  8. Experiments – dataset:14 financial indicators

  9. Experiments –

  10. Experiments– feature plan

  11. Experiments

  12. Experiments – Clustering of the SOTM

  13. Experiments

  14. Conclusions • The SOTM can identify multivariate structural changes in data. • This the SOTM opens the door for early identification of imbalances that expose economies to financialcrises.

  15. Comments • Advantages • The SOTM uses visual dynamic clustering difference from traditional statistical methods. • Applications • Self-Organizing Time Map • Financial stability surveillance

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