@PhdThesis{diss_mods_00002606,
  author = 	{Edler, Lars},
  title = 	{Analysing Economic Data with Self-Organizing Maps - A Geometric Neural Network Approach},
  year = 	{2007},
  publisher = 	{Christian-Albrechts-Universit{\"a}t zu Kiel},
  address = 	{Kiel},
  keywords = 	{SOM; Data-mining; forecasting; financial markets; self-organizing maps; neural networks; pattern recognition},
  abstract = 	{Self-Organizing Maps (SOM) are a special form of Neural Networks that use unsupervised learning and auto-classification of data. Therefore, SOM is a very flexible algorithm which is in particular well-suited to identify unexpected structures in complex (and multidimensional) data sets. We use SOM in order to build feature-domain models, i.e. we rather focus on the geometric or symbolic characteristics of patterns within a time series than on their respective location in time. In a next step we try to extract valuable information from the discovered features in order to forecast out-of-sample. We employ the proposed method with different financial time series and test for its performance by means of a set of non-parametric tests. Moreover, the SOM is employed as a clustering algorithm. The method is used in order to form homogenous groups out of 55 countries only by looking at a set of macro data. Without giving any learning guidelines and/or model restrictions the SOM turns out to be a powerful tool for the identification of clusters in the data through its self-organising behaviour.},
  url = 	{https://macau.uni-kiel.de/receive/diss_mods_00002606},
  file = 	{:https://macau.uni-kiel.de/servlets/MCRFileNodeServlet/dissertation_derivate_00002280/Dissertation_Revised.pdf:PDF},
  language = 	{en}
}