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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2021
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| Subjects: | |
| 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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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | 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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