Improving artificial neural networks’ performance in seasonal time series forecasting

In this study, an artificial neural network (ANN) structure is proposed for seasonal time series forecasting. The proposed structure considers the seasonal period in time series in order to determine the number of input and output neurons. The model was tested for four real-world time series. The results found by the proposed ANN were compared with the results of traditional statistical models and other ANN architectures. This comparison shows that the proposed model comes with lower prediction error than other methods. It is shown that the proposed model is especially convenient when the seasonality in time series is strong; however, if the seasonality is weak, different network structures may be more suitable.