Sharif University of Technology Scientia Iranica 1026-3098 23 2 2016 04 01 A Feedback-oriented Data Delay Modeling in a Dynamic Neural Network for Time Series Forecasting A Feedback-oriented Data Delay Modeling in a Dynamic Neural Network for Time Series Forecasting 711 720 3857 10.24200/sci.2016.3857 EN Mohammad Namakshenas Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151-159, Iran Amirhossein Amiri Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151-159, Iran 0000-0002-2385-8910 Rashed Sahraeian Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151-159, Iran 0000-0003-0613-3595 Journal Article 2015 04 14 In this study, we develop a neural network with a time shifting approach to forecast time series patterns. We investigate the impact of different layer-weight configurations to capture the trends in the forms of seasonal, chaotic, etc. We also hypothesize the combined effect of the delayed inputs and the forward connections to introduce a dynamical structure. The effect of overfitting issue is procedurally monitored to gain the resistance property from the early stoppage of training process and to reduce the predictions' error. Finally, the performance of the proposed network is challenged by six well-known deterministic and non-deterministic time series and compared by the autoregression (AR), artificial neural network (ANN), adaptive k-nearest neighbors (AKN), and adaptive neural network (ADNN) models. The results show that the proposed network outperforms the conventional models, particularly in forecasting the chaotic and seasonal time series. https://scientiairanica.sharif.edu/article_3857_f269cb7796c3319c9aa4d146b52139e6.pdf