An automatic calibration framework applied on a metaheuristic fuzzy model for the CIF competition

The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current paper improves the previous work of Coelho et al. [1], [2], introduced in the 2014 WCCI, which introduced a Hybrid Self-adaptive Forecasting Model (HFM), calibrated using an evolutionary metaheuristic. Here, an expert input selection assisted by the use of Neighborhood Structures is used for learning the dataset provide in the CIF 2016 forecasting competition. Two different metaheuristic algorithms are used for training the HFM, computational experiments are conducted for optimizing both models. By selecting the maximum lag to be used by our model and, consequently, defining a rate of training cycles, we calibrate the HFM in order to minimize Symmetric Mean Absolute Percentage Errors. Blind forecasts generated by the model will be submit to the competition.