A feedback-oriented data delay modeling in a dynamic neural network for time series forecasting

In this study, we develop a neural network with a time shifting approach to forecast time series patterns. we investigate the impact of dierent layer-weight configurations to capture the trends in seasonal, chaotic, etc. forms. 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 error of predictions. 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.