On interpretation of fuzzy cognitive maps trained to model time series

Abstract

The article analyzes consecutive phases of time series modelling with Fuzzy Cognitive Maps. The subject of interest are features determining models of good quality. First, we present the procedure: design phase, learning phase, and in the end - application. The discussion is illustrated with experiments on two synthetic time series. We have shown that the design phase determines qualitative and quantitative effectiveness of modelling. We have addressed effects of misdesigns: too large, too small or unfit at all maps on modelling quality.