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- W2480208918 abstract "In this chapter, the authors investigate the use of advanced machine learning and data analysis methods for analyzing the early warning signals of currency crisis. So far the empirical studies on economic crises have focused on conventional economic modeling methods. Their study aims to see if new insights can be gained from the application of other methods, more specifically neural network based clustering analysis methods such as the SOM (Self-Organizing Maps), in analyzing important economic indicators for the prediction of financial crises. The authors shall first give a review of studies on crisis early warning systems, introduce the SOM method, then present and discuss the results from analyzing the 1992 Finnish currency crisis using the SOM." @default.
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- W2480208918 date "2010-05-26" @default.
- W2480208918 modified "2023-09-25" @default.
- W2480208918 title "A Visualization and Clustering Approach to Analyzing the Early Warning Signals of Currency Crises" @default.
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- W2480208918 doi "https://doi.org/10.4018/978-1-61520-629-2.ch004" @default.
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