Economic states on neuronic maps

Abstract

We test the idea of visualizing economic statistics data on self-organization related maps, which are the LLE, ISOMAP and GTM maps. We report initial results of this work. These three maps all have distinguished theoretical foundations. The statistic data usually span high-dimensional space, sometimes more than 10 dimensions. To perceive these data as a whole and to foresee future trends, perspective visualization assistance is an important issue. We use economic statistics for the United States over the past 25 years (1977 to 2001) and apply them on the maps. The results from these three maps display historic events along with their trends and significance.