Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast
The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and opti...
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Universitas Ahmad Dahlan
2021
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Online Access: | http://eprints.utm.my/id/eprint/96453/1/SharifahHafizah2021_AnalysisofHybridNonLinearAutoregressive.pdf http://eprints.utm.my/id/eprint/96453/ http://dx.doi.org/10.12928/TELKOMNIKA.v19i4.17024 |
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my.utm.964532022-07-24T10:26:52Z http://eprints.utm.my/id/eprint/96453/ Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast Hassan, Mohamed Khalafalla Syed Ariffin, Sharifah Hafizah Syed Yusof, Sharifah Kamilah Ghazali, N. Effiyana Ahmed Kanona, Mohammed Eltayeb TK Electrical engineering. Electronics Nuclear engineering The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests. Universitas Ahmad Dahlan 2021-08 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/96453/1/SharifahHafizah2021_AnalysisofHybridNonLinearAutoregressive.pdf Hassan, Mohamed Khalafalla and Syed Ariffin, Sharifah Hafizah and Syed Yusof, Sharifah Kamilah and Ghazali, N. Effiyana and Ahmed Kanona, Mohammed Eltayeb (2021) Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast. Telkomnika (Telecommunication Computing Electronics and Control), 19 (4). pp. 1078-1089. ISSN 1693-6930 http://dx.doi.org/10.12928/TELKOMNIKA.v19i4.17024 DOI:10.12928/TELKOMNIKA.v19i4.17024 |
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TK Electrical engineering. Electronics Nuclear engineering Hassan, Mohamed Khalafalla Syed Ariffin, Sharifah Hafizah Syed Yusof, Sharifah Kamilah Ghazali, N. Effiyana Ahmed Kanona, Mohammed Eltayeb Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
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The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests. |
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Article |
author |
Hassan, Mohamed Khalafalla Syed Ariffin, Sharifah Hafizah Syed Yusof, Sharifah Kamilah Ghazali, N. Effiyana Ahmed Kanona, Mohammed Eltayeb |
author_facet |
Hassan, Mohamed Khalafalla Syed Ariffin, Sharifah Hafizah Syed Yusof, Sharifah Kamilah Ghazali, N. Effiyana Ahmed Kanona, Mohammed Eltayeb |
author_sort |
Hassan, Mohamed Khalafalla |
title |
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
title_short |
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
title_full |
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
title_fullStr |
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
title_full_unstemmed |
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
title_sort |
analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2021 |
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http://eprints.utm.my/id/eprint/96453/1/SharifahHafizah2021_AnalysisofHybridNonLinearAutoregressive.pdf http://eprints.utm.my/id/eprint/96453/ http://dx.doi.org/10.12928/TELKOMNIKA.v19i4.17024 |
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