In recent years, studies including long memory time series are existed in the literature. Such time
series in real life may have both linear and nonlinear structures. Linear models are inadequate for this kind
of time series. An alternative method to forecast these time series is artificial neural networks which is data
based and can model both linear and nonlinear structure in these time series. In order to determine the
number of nodes in the layers of a network is an important decision. This decision has been made by using
various architecture selection criteria. The performance of these criteria varies, depending on components of
time series, such as trend and seasonality. In this study, some architecture selection criteria are compared on
real time series when artificial neural networks are employed in forecasting. Some advices are given for
using artificial neural networks to forecast long memory time series.
Keywords: Architecture selection criteria, Artificial neural networks, Long range dependent, Time series.
