A radial basis function neural network-based coevolutionary algorithm for short-term to long-term time series forecasting

Abstract

This work analyzes the behavior and effectiveness of the L-Co-R method using a growing horizon to predict. This algorithm performs a double goal, on the one hand, it builds the architecture of the net with a set of RBFNs, and on the other hand, it sets a group of time lags in order to forecast future values of a time series given. For that, it has been used a set of 20 time series, 6 different methods found in the literature, 4 distinct forecast horizons, and 3 distinct quality measures have been utilized for checking the results. In addition, a statistical study has been done to confirms the good results of the method L-Co-R.