Forecasting methods are very important for the development of various activities in everyday society. Several statistical models have been developed, but many assumptions must be made in order to obtain an acceptable response. Nonstatistical models for time series forecasting such as those involving systems Fuzzy Inference Systems (FIS) provide a description of the process through linguistic rules. This dissertation delves into GPFISForecast: a version of GPFIS - Fuzzy Inference System based on Multigene Genetic Programming - for univariate time series forecasting. This model consists of four basic stages: Fuzzification, Inference, Defuzzification and Evaluation. In each of these steps, different configurations will have distinct impacts on the results. This work proposes the improvement of GPFIS-Forecast along two main lines (i) increase the amount of possible configurations and assess their contribution to a better forecasting accuracy and (ii) add further information to the interpretation of results, keeping in mind both accuracy and interpretability. The case studies show that in the case of time series with small tendency, GPFIS-Forecast provides a good accuracy; when tendency is larger and pre-processing becomes necessary, interpretability is affected. The Fuzzy Forecasting Limits introduced here add more information to the result, pointing to possible adjustments to rule bases of models with greater granularity.