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

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab Methodology 1.. M

6. Intelligent Database Systems Lab Methodology 1.. M

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

8. Intelligent Database Systems Lab Experiments – dataset:14 financial indicators

9. Intelligent Database Systems Lab Experiments –

10. Intelligent Database Systems Lab Experiments– feature plan

11. Intelligent Database Systems Lab Experiments

12. Intelligent Database Systems Lab Experiments – Clustering of the SOTM

13. Intelligent Database Systems Lab Experiments

14. Intelligent Database Systems Lab 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 financial crises.

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