This paper intends to identify empirically the common characteristics of bankrupting firms from accounting and financial data. It provides not only ways of predicting firms headed for bankruptcy, but also identifying the pattern of failing firms over time. The Adaptive Learning Network (ALN), an...

„h Contrary to common perceptions, most Australian businesses survive for a considerable time ¡V for example, around two¡Vthirds of businesses are still operating after five years and almost one¡Vhalf are still operating after ten years. „h Around 7.5 per cent of businesses exit each year...

The Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore...

In high dimensional data modeling, Multivariate Adaptive Regression Splines (MARS) is a popular nonparametric regression technique used to define the nonlinear relationship between a response variable and the predictors with the help of splines. MARS uses piecewise linear functions for local fit...

We forecast hourly solar irradiance time series using a novel hybrid model based on SOM (self-organizing maps), SVR (support vector regression) and PSO (particle swarm optimization). In order to solve the noise and stationarity problems in the statistical time series forecasting modelling...

A new, efficient clustering method for solving the cellular manufacturing problem is presented in this paper. The method uses the part-machine incidence matrix of the manufacturing system to form machine cells, each of which processes a family of parts. By doing so, the system is decomposed into...

Exploratory analysis of financial and economic data is being accepted as a very valuable and important stage of solving of majority of financial and economic tasks. We introduce a short overview of neural network approaches and applications in the financial and economic domain. Self-organizing...

Self-organizing maps (SOM) are unsupervised, competitive neural networks used to project high-dimensional data onto a low-dimensial space. In this article we show how SOM can be sued to draw graphs in the plane. The SOM-based approach to graph drawing, which belongs to the general class of...

The authors explore the effect of different topologies on properties of self-organizing maps (SOM). They suggest several diagnostics for measuring topology-induced errors in SOM and use these in a comparison of four different topologies. The results show that SOM is less sensitive to localized...