presenter : chang, shih-jie authors : peter sarlin * 2013.prl
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Intelligent Database Systems Lab
Presenter : CHANG, SHIH-JIE
Authors : Peter Sarlin*
2013.PRL
Decomposing the global financial crisis: A Self-Organizing Time Map
Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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.
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.
Intelligent Database Systems Lab
Methodology
1 . .M
Intelligent Database Systems Lab
Methodology 1 . .M
Intelligent Database Systems Lab
Methodology
Silhouette coefficient
K l
100+150100*150
=60*10=600
100+900100*900 =90*10=900. 10
. 10
Intelligent Database Systems Lab
Experiments – dataset:14 financial indicators
Intelligent Database Systems Lab
Experiments –
Intelligent Database Systems Lab
Experiments– feature plan
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments – Clustering of the SOTM
Intelligent Database Systems Lab
Experiments
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.
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
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