Comparison of Architecture Selection Sriteria in Analyzing Long Memory Time Series

Abstract

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. © 2012 Bentham Science Publishers. All rights reserved.