Short, medium and long term forecasting of time series using the L-Co-R algorithm
This paper describes the coevolutionary algorithm L-Co-R (Lags COevolving with Radial Basis Function Neural Networks  RBFNs), and analyzes its performance in the forecasting of time series in the short, medium and long terms. The method allows the coevolution, in a single process, of the RBFNs as the time series models, as well as the set of lags to be used for predictions, integrating two genetic algorithms with real and binary codification, respectively. The individuals of one population are radial basis neural networks (used as models), while sets of candidate lags are individuals of the second population. In order to test the behavior of the algorithm in a new context of a variable horizon, 5 different measures have been analyzed, for more than 30 different databases, comparing this algorithm against six existing algorithms and for seven different prediction horizons. Statistical analysis of the results shows that L-Co-R outperforms other methods, regardless of the horizon, and is capable of predicting short, medium or long horizons using real known values.
coevolutionary algorithms neural networks time series forecasting significant lags variable term horizon