A New Architecture Selection Strategy in Solving Seasonal Autoregressive Time Series by Artificial Neural Networks
Çağdaş Hakan Aladağ, Erol Eğrioğlu, Süleyman Günay · DergiPark (Istanbul University) · 2008
The only suggestions given in the literature for determining the architecture 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.