Macroeconomic time series prediction using prediction networks and evolutionary algorithms
P. Forsberg, Mattias Wahde · WIT transactions on modelling and simulation · 2006
The prediction of macroeconomic time series by means of a form of fully recurrent neural networks, called discrete-time prediction networks (DTPNs), is considered.The DTPNs are generated using an evolutionary algorithm, allowing both structural and parametric modifications of the networks, as well as modifications in the squashing function of individual neurons.The results show that the evolved DTPNs achieve better performance on both training and validation data compared to benchmark prediction methods.The importance of allowing structural modifications in the evolving networks is discussed.Finally, a brief investigation of predictability measures is presented.