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A new model selection strategy in artificial neural networks

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<mark>Journal publication date</mark>1/02/2008
<mark>Journal</mark>Applied Mathematics and Computation
Issue number2
Volume195
Number of pages7
Pages (from-to)591-597
Publication StatusPublished
<mark>Original language</mark>English

Abstract

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 new model selection strategy based on well-known model selection criteria is proposed. Proposed strategy is applied to real and simulated time series. Moreover, a new direction accuracy criterion called modified direction accuracy criterion is discussed. The new model selection strategy is more reliable than known model selection criteria.