Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting
A time-series data analysis and prediction tool for learning the network traffic usage data is very important in order to ensure an acceptable and a good quality of network services can be provided to the organization (e.g., university). This paper presents the modeling using a nonlinear autoregress...
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my.ums.eprints.302692021-09-06T05:07:12Z https://eprints.ums.edu.my/id/eprint/30269/ Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting Haviluddin Haviluddin Rayner Alfred TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television A time-series data analysis and prediction tool for learning the network traffic usage data is very important in order to ensure an acceptable and a good quality of network services can be provided to the organization (e.g., university). This paper presents the modeling using a nonlinear autoregressive with eXogenous input (NARX) algorithm for predicting network traffic datasets. The best performance of NARX model, based on the architecture 189:31:94 or 60%:10%:30%, with delay value of 5, is able to produce a pretty good with Mean Squared Error of 0.006717 with the value of correlation coefficient, r, of 0.90764 respectively. In short, the NARX technique has been proven to learn network traffic effectively with an acceptable predictive accuracy result obtained. 2015 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30269/1/Performance%20of%20modeling%20time%20series%20using%20nonlinear%20autoregressive%20with%20eXogenous%20input%20%28NARX%29%20in%20the%20network%20traffic%20forecasting%20ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/30269/4/Performance%20of%20modeling%20time%20series%20using%20nonlinear%20autoregressive%20with%20eXogenous%20input%20%28NARX%29%20in%20the%20network%20traffic%20forecasting.pdf Haviluddin Haviluddin and Rayner Alfred (2015) Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting. In: 2015 International Conference on Science in Information Technology (ICSITech), 27-28 October 2015, Yogyakarta, Indonesia. https://ieeexplore.ieee.org/document/7407797/keywords#keywords |
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Universiti Malaysia Sabah |
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UMS Library |
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Asia |
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Malaysia |
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Universiti Malaysia Sabah |
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http://eprints.ums.edu.my/ |
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English English |
| topic |
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television |
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TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Haviluddin Haviluddin Rayner Alfred Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| description |
A time-series data analysis and prediction tool for learning the network traffic usage data is very important in order to ensure an acceptable and a good quality of network services can be provided to the organization (e.g., university). This paper presents the modeling using a nonlinear autoregressive with eXogenous input (NARX) algorithm for predicting network traffic datasets. The best performance of NARX model, based on the architecture 189:31:94 or 60%:10%:30%, with delay value of 5, is able to produce a pretty good with Mean Squared Error of 0.006717 with the value of correlation coefficient, r, of 0.90764 respectively. In short, the NARX technique has been proven to learn network traffic effectively with an acceptable predictive accuracy result obtained. |
| format |
Conference or Workshop Item |
| author |
Haviluddin Haviluddin Rayner Alfred |
| author_facet |
Haviluddin Haviluddin Rayner Alfred |
| author_sort |
Haviluddin Haviluddin |
| title |
Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| title_short |
Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| title_full |
Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| title_fullStr |
Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| title_full_unstemmed |
Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting |
| title_sort |
performance of modeling time series using nonlinear autoregressive with exogenous input (narx) in the network traffic forecasting |
| publishDate |
2015 |
| url |
https://eprints.ums.edu.my/id/eprint/30269/1/Performance%20of%20modeling%20time%20series%20using%20nonlinear%20autoregressive%20with%20eXogenous%20input%20%28NARX%29%20in%20the%20network%20traffic%20forecasting%20ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/30269/4/Performance%20of%20modeling%20time%20series%20using%20nonlinear%20autoregressive%20with%20eXogenous%20input%20%28NARX%29%20in%20the%20network%20traffic%20forecasting.pdf https://eprints.ums.edu.my/id/eprint/30269/ https://ieeexplore.ieee.org/document/7407797/keywords#keywords |
| _version_ |
1760230741013168128 |
| score |
13.251813 |