Forecast combining using a generalized single multiplicative neuron
Juan David Velasquez, Cristian Zambrano, Carlos Jaime Franco · IEEE Latin America Transactions · 2014
Forecast combining is an important technique for increasing the accuracy of the forecasts obtained using different alternative models. In this article, we propose the use of a generalized single multiplicative neuron as a nonlinear combiner inside of a forecasts combination model. Numerical evidences indicate that, at least for the experimental case, the multiplicative neuron is able to obtain forecasts more accurate that each individual model and the forecasts combination using a simple arithmetical average.