Development of a fuzzy rule-based system using Genetic Programming for Forecasting problems

This work presents a novel genetic fuzzy system for forecasting, called Genetic Programming Fuzzy Inference System for Forecasting problems (GPFIS-Forecast), which generates an interpretable fuzzy rule base by using Multi-Gene Genetic Programming to define the premises terms of fuzzy rules. The main differences between GPFIS-Forecast and other genetic fuzzy systems lie in its fuzzy inference process, because it: (i) enables premises to be include negation, t-conorm and linguistic hedge operators; (ii) applies methods to define a consequent term more compatible with a given premise; and (iii) makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. GPFIS-Forecast has been tested in the NN3 Competition, in order to evaluate its performance in a benchmark problem. In this case, it has produced competitive results when compared to other forecasting approaches.