Predicting system collapse : application of kernel-based machine learning and inclination analysis

Abstract

While many modelling methods have been developed and introduced to predict the actual state of a system at the next point of time, the purpose of this research is to present and discuss two approaches NOT to predict the exact future states, but to identify the potential for final collapse of a system. The first approach is based on kernel methods, a sub category of supervised learning, and attempts to provide a visualization method to classify the active and dead companies and predict the potential collapse of a system. The second method aims to analyze the inclination of a system by looking at the local changes that have been observed over a certain period of time in the past. Application of these modelling approaches to predict collapse in different companies belonging to two industrial sectors by looking at behaviour of their closing stock prices are discussed in this research. Advantages and limitations of each approach are also discussed.