TY - JOUR
KW - Time Series
KW - Error Feedback
KW - Nonlinear Autoregressive Moving-Average Model
KW - Recurrent Network
AU - Waddah Waheeb
AU - Rozaida Ghazali
AB - In 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.
IS - Regular Issue
M1 - 5
N2 - In 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.
PY - 2019
SP - 126
EP - 133
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models
UR - https://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_15_pdf_10586.pdf
VL - 5
SN - 1989-1660
ER -