Adaptive Weighted Information Criterion to Determine the Best Architecture
Pp. 34-39 (6)
Cagdas Hakan Aladag and Erol Eǧrioǧlu
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.  proposed a criterion
which can measure all properties of forecasts. Aladag et al.  improved the criterion proposed by 
by using optimization. In this study, both the weighted information criterion proposed by Eǧrioǧlu et al.
 and the adaptive weighted information criterion proposed by Aladag et al.  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.
Artificial neural networks, Forecasting, Model selection criterion, Time series.
Hacettepe University, Faculty of Science, Department of Statistics, 06800, Ankara, Turkey