Application of prediction models using fuzzy sets: an Bayesian inspired approach

A fuzzy inference framework based on fuzzy relations is explored and applied to a real set of simulated forecasts and experimental data referring to temperature and humidity in specific coffee crop sites in Brazil. In short, the used model consists of fuzzy relations over possibility distributions, resulting in a fuzzy model analog to a Bayesian inference process. The application of the fuzzy model to temperature and humidity data resulted in a set of revised forecasts, which were later compared to the correspondent set of experimental data using two different statistical measures of accuracy, MAPE (mean absolute percentage error) and Willmott D. Statistical results were confronted to the original simulated forecast fit to experimental data, showing that the methodology was, in most cases, able to improve the specialist’s forecasts in both statistical measures.