Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

Abstract

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.

Refining disparity maps using deep learning and edge-aware smoothing filter

“…Mismatches are interpolated with median value of nearest matching neighbors, while occlusions use disparity value of nearest matching neighbor on right (or left) or raw disparity value if no match found. The quality of the disparity map is enhanced further through the application of EASF which is a modification of the traditional edge-preserving filter [38], [39] that iteratively applies the filter, smoothing the image while preserving its edges [40]. The idea is that the amount of smoothing between two pixels should depend on how far apart they are.…”

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

“…Minimizing the effects of low-and high-frequency noise can help accurately forecast the short-or long-term-scale data. Some earlier studies discussed the importance of noise removal or data processing [7,18,22,24,29]. In the subsequent section, we discuss the different local smoothing methods applied in this study.…”

“…Next-generation networks have been designed to offer reliable service with ultra-low latency, massive-scale connectivity, high security, extreme data rates, optimized energy, and better quality of service (QoS) [1][2][3]. Despite these features, the technology (infrastructure and logic) used in these networks must display an intelligence for coping with the dynamic QoS demand [4][5][6][7][8][9] and react autonomously to different dynamic and self-organizing situations. Additionally, network management is complicated due to the coupling between various service layers where congestions can arise and spread vertically as well as horizontally.…”

“…Thus, the accuracy of predictive approaches was regarded as a vital factor and essential in various applications of the predictive frameworks. Reliable artificial intelligence (AI) and machine learning (ML) techniques are crucial and widely used in different applications, such as network traffic forecasts [4][5][6][7][8][9]14], the Internet of things (IoT) [10], and wireless communications [11,15]. The data characteristics indicated that the traffic used in real-time applications in current and future networks exhibited variable, nonlinear, and unstructured data formats with slowly decaying autocorrelations between different samples.…”

Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting

“…Statistical analysis models are based on the generalized autoregressive integrated moving average (ARIMA) model, while most traffic forecasting models are based on supervised ML, in particular, artificial neural networks (ANNs) [1], [2]. ARIMA-based models fall short when dealing with nonlinear and non-stationary data as ARIMA requires a stationary property to be imposed [6]- [16] unlike ANN. This paper presents the results of the study on the effect of hybrid long-term short-term memory (LSTM) neural network and local smoothing techniques.…”