01446nas a2200217   4500000000100000000000100001008004100002260001200043653001600055653001900071653005000090653002200140100001800162700002000180245012000200856009700320300001200417490000600429520077900435022001401214       2019                            d  c06/201910aTime Series10aError Feedback10aNonlinear Autoregressive Moving-Average Model10aRecurrent Network1 aWaddah Waheeb1 aRozaida Ghazali00aForecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_15_pdf_10586.pdf  a126-1330 v53 aIn this paper, a new application of ridge polynomial based neural network models in multivariate time series forecasting is presented. The existing ridge polynomial based neural network models can be grouped into two groups. Group A consists of models that use only autoregressive inputs, whereas Group B consists of models that use autoregressive and moving-average (i.e., error feedback) inputs. The well-known Box-Jenkins gas furnace multivariate time series was used in the forecasting comparison between the two groups. Simulation results show that the models in Group B achieve significant forecasting performance as compared to the models in Group A. Therefore, the Box-Jenkins gas furnace data can be modeled better using neural networks when error feedback is used.  a1989-1660