In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a ne...

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this paper, we propose a new criterion for model selection named the subspace information criterion (SIC). Computer simulations show that SIC works well even when the number of training examples is small.

1: In this paper we discuss measures of information and divergence and model selection criteria. Three classes of measures, Fisher-type, divergencetype and entropy-type measures, are discussed and their properties are presented. Information through censoring and truncation is presented and model selection criteria are investigated including the Akaike Information Criterion (AIC) and the Diverge...