Time series forecasting: Automatic determination of lags and radial basis neural networks for a changing horizon environment

This paper shows how E-tsRBF deals with time-series prediction in a changing horizon environment. E-tsRBF is a meta-evolutionary algorithm that simultaneously evolves both the neural networks and the set of lags needed to forecast time series. The method uses radial basis function neural networks, a kind of net that has been successfully applied to time series prediction in literature. Frequently, methods to build and train these networks must be given the past periods or lags to be used in order to create patterns and forecast any time series. Up to twenty-one time series are evaluated in this work, showing the behaviour of the new method.