Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models

TitleForecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models
Publication TypeJournal Article
Year of Publication2019
AuthorsWaheeb, W., and R. Ghazali
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume5
Number5
Date Published06/2019
Pagination126-133
Abstract

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.

KeywordsError Feedback, Nonlinear Autoregressive Moving-Average Model, Recurrent Network, Time Series
DOI10.9781/ijimai.2019.04.004
URLhttps://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_15_pdf_10586.pdf
AttachmentSize
ijimai_5_5_15.pdf1.13 MB