Time series forecasting is an important tool to support both individual and organizational
decisions (eg planning production resources). In recent years, a large literature has evolved
on the use of evolutionary artificial neural networks (EANN) in many forecasting
applications. Evolving neural networks are particularly appealing because of their ability to
model an unspecified nonlinear relationship between time series variables. In this work, two
new approaches of a previous system, automatic design of artificial neural networks …

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 non-linear relationship between time series variables.
This paper evaluates two methods to evolve neural networks architectures, one carried out …