Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models
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 Gro...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2019-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/node/3061 |
| _version_ | 1828880002335113216 |
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| author | Waddah Waheeb Rozaida Ghazali |
| author_facet | Waddah Waheeb Rozaida Ghazali |
| author_sort | Waddah Waheeb |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T09:39:22Z |
| format | Article |
| id | doaj.art-716f005da3cf495993babcd6234a63cd |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-13T09:39:22Z |
| publishDate | 2019-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-716f005da3cf495993babcd6234a63cd2022-12-21T23:52:14ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602019-06-015512613310.9781/ijimai.2019.04.004ijimai.2019.04.004Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network ModelsWaddah WaheebRozaida GhazaliIn 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.http://www.ijimai.org/journal/node/3061Error FeedbackNonlinear Autoregressive Moving-Average ModelRecurrent NetworkTime Series |
| spellingShingle | Waddah Waheeb Rozaida Ghazali Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models International Journal of Interactive Multimedia and Artificial Intelligence Error Feedback Nonlinear Autoregressive Moving-Average Model Recurrent Network Time Series |
| title | Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models |
| title_full | Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models |
| title_fullStr | Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models |
| title_full_unstemmed | Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models |
| title_short | Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models |
| title_sort | forecasting the behavior of gas furnace multivariate time series using ridge polynomial based neural network models |
| topic | Error Feedback Nonlinear Autoregressive Moving-Average Model Recurrent Network Time Series |
| url | http://www.ijimai.org/journal/node/3061 |
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