Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …

Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

In medical imaging, denoising is very important for analysis of images, diagnosis and
treatment of diseases. Currently, image denoising methods based on deep learning are …

With the rapid development of wireless networks, the self-management and active
adjustment capabilities of base stations have become crucial. The accurate prediction of …

With evolution toward the fifth generation (5G) cellular technologies, forecasting and
understanding of mobile Internet traffic based on big data is the foundation to enable …

Traffic control and management applications require the full realization of traffic flow data.
Frequently, such data are acquired by traffic sensors with two issues: it is not practicable or …

This paper presents a review of the literature on network traffic prediction, while also serving
as a tutorial to the topic. We examine works based on autoregressive moving average …

Accurate real‐time traffic prediction is required in many networking applications like dynamic
resource allocation and power management. This paper explores a number of predictors …