Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution

Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANNs) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. This paper evaluates two methods to evolve neural networks architectures, one carried out with genetic algorithm and a second one carry out with differential evolution algorithm. A comparative study between these two methods, with a set of referenced time series will be shown. The object of this study is to try to improve the final forecasting getting an accurate system.