Abstract

In the literature, different selection criteria are used for determining the best architecture when time series is analyzed by artificial neural networks. Criteria available in the literature measure different properties of forecasts. To obtain better forecasts, Eǧrioǧlu et al. [1] proposed a criterion which can measure all properties of forecasts. Aladag et al. [2] improved the criterion proposed by [1] by using optimization. In this study, both the weighted information criterion proposed by Eǧrioǧlu et al. [1] and the adaptive weighted information criterion proposed by Aladag et al. [2] are introduced. These criteria are used in the architecture selection to analyze time series which are the import values of Turkey and the air pollution records in Ankara. As a result of computations, obtained results are compared and discussed. As a result of the comparison, it is seen that adaptive weighted information criterion produce more consistent results.