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Stacking-based GRNN-SGTM Ensemble Model for Prediction Tasks

An effective solution of the prediction tasks requires the high accuracy of the result with minimal resource and time costs for the operation of the chosen algorithm. In cases when high accuracy of the received result has the first priority, it is expedient to use ensemble learning. This paper describes a prediction method using a new, stacking-based GRNN ensemble model. Each member of the developed ensemble processes its own dataset, where the vectors of the original set of data are randomly shifted relative to the current point. The authors chose SGTM neural-like structure as a meta-algorithm for the formation of the result of the ensemble. This choice is argued by the high accuracy and speed of its work. The results of a number of experimental studies on the optimal parameters selection of the developed ensemble are described. A comparison of the efficiency of its work with a number of known predictors was done. Prospects for further research are described.