Causal Relevancy: a New Concept to Improve the Prediction Accuracy of Dynamical Systems Using Inductive Reasoning

In this paper1, the concept of Causal Relevancy. (CR) is introduced in the context of the Fuzzy Inductive Reasoning (FIR) modeling and simulation methodology. The idea behind causal relevancy is to quantify how much in uence each system variable has, from the spatial and temporal points of view, on the prediction of the output. This paper introduces the fuzzy inductive reasoning inference engine, and describes how it can be improved by means of the causal relevancy concept, helping to overcome some shortcomings during the forecast stage. The FIR inference engine is based on the k{nearest neighbor classication rule, commonly used in the eld of Pattern Recognition, and uses a Euclidean distance measure to compute the distance between neighbors. In this paper a weight{Euclidean distance measure is proposed that is able to nd better quality neighbors by using the causal relevancy concept. Applications from di erent elds are studied in the light of the prediction process, and a comparison between the accuracy of the predictions obtained when using the classical inference engine and the causal relevancy option is performed. The results obtained from this research show that FIR predictions are more accurate and precise when the CR option is used, especially for systems where classical FIR forecasting is rather poor.