TELKOMNIKA Telecommunication, Computing, Electronics and Control
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

Autoregressive neural network
Bandwidth slice
Forecast
Local smoothing

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.

Mohamed Khalafalla Hassan, Sharifah H. S. Ariffin, Sharifah Kamilah Syed-Yusof, N. Effiyana Ghazali, Mohammed EA Kanona

http://journal.uad.ac.id/index.php/TELKOMNIKA

Apr 21, 2021

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