Reduction of complexity in scenario analysis by means of dynamic fuzzy data analysis

One goal of scenario analysis is to investigate possible future developments. In order to cover almost all alternatives it is desirable to analyze as many different scenarios as possible. On the other hand the complexity of the analysis grows as the number of scenarios increases. This often limits the number of scenarios considered. At this point dynamic fuzzy data analysis can be used. It offers methods to cluster objects (i.e. scenarios) which are represented by trajectories over time, therefore reducing complexity by extracting a small set of typical scenarios out of a large set of possible scenarios. Thereafter these typical scenarios can be interpreted by an expert. This paper describes such a dynamic fuzzy data analysis method and describes how it can be used in scenario analysis.