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What is The Self-Organizing Map (SOM)

Handbook of Research on Computational Science and Engineering: Theory and Practice
The Self-organizing map is a non-parametric and non-linear neural network that explores data using unsupervised learning. The SOM can produce output that maps multidimensional data onto a two-dimensional topological map. Moreover, since the SOM requires little a priori knowledge of the data, it is an extremely useful tool for exploratory analyses. Thus, the SOM is an ideal visualization tool for analyzing complex time-series data.
Published in Chapter:
Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach
Peter Sarlin (Åbo Akademi University, Finland)
DOI: 10.4018/978-1-61350-116-0.ch017
Since the 1980s, two severe global waves of sovereign defaults have occurred in less developed countries (LDCs): the LDC defaults in the 1980s and the LDC defaults at the turn of the 21st century. To date, the topic is contemporary, while the forecasting and monitoring results of debt crises are still at a preliminary stage. This chapter explores whether the application of the Self-Organizing Map (SOM), a neural network-based visualization tool, facilitates the monitoring of multidimensional financial data. Thus, this chapter presents a SOM model for visualizing the evolution of sovereign debt crises’ indicators. The results of this chapter indicate that the SOM is a feasible tool for visualization of early warning signals of sovereign defaults.
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