A multistrategy approach to the classification of phases in business cycles

Abstract

The classification of business cycles is a hard and important problem. Government as well as business decisions rely on the assessment of the current business cycle. In this paper, we investigate how economists can be better supported by a combination of machine learning techniques. We have successfully applied Inductive Logic Programming (ILP). For establishing time and value intervals different discretization procedures are discussed. The rule sets learned from different experiments were analyzed with respect to correlations in order to find a concept drift or shift.