E-tsRBF: Preliminary Results on the Simultaneous Determination of Time-Lags and Parameters of Radial Basis Function Neural Networks for Time Series Forecasting

摘要

Radial basis function neural networks have 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. This paper introduces E-tsRBF, a meta-evolutionary algorithm that evolves both the neural networks and the set of lags needed to forecast time series at the same time. Up to twenty-one time series are evaluated in this work, showing the behavior of the new method.