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A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks

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<mark>Journal publication date</mark>1/12/2008
<mark>Journal</mark>Hacettepe Journal of Mathematics and Statistics
Issue number2
Volume37
Number of pages16
Pages (from-to)185-200
Publication StatusPublished
<mark>Original language</mark>English

Abstract

The only suggestions given in the literature for determining the archi- tecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results of the simulation study described in this paper, a new architecture selection strategy is proposed and shown to work well. It is noted that although in some studies the period of a seasonal time series has been taken as the number of inputs of the neural network model, it is found in this study that the period of a seasonal time series is not a parameter in determining the number of inputs.