An Artificial Neural Networks Based Dynamic Decision Model for Time-Series Forecasting

The forecasting models for time series forecasting using computational intelligence such as artificial neural networks (ANNs) , genetic programming (GP) and gene expression programming (GEP), especially hybrid particle swarm optimization (PSO) algorithm and artificial neural networks (ANNs) have achieved favorable results. However, these studies, have assumed a static environment. This paper investigates the development of a new dynamic decision forecasting model. The input size of the ANNs will be dynamical changed in the process of evolution. Application results prove the higher precision and generalization capacity obtained by this new method than the static models.