# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 11 Conclusions and Future Opportunities  
Communication networks, wired and wireless, continue to grow in size, complexity, and intelligence. Network intelligence is introduced for the network to handle high-volume situations better and to ensure the quality of service. This paper has surveyed multiple papers relevant to network traffic prediction and classified them according to their use and applicability to the short term or the long term. These methodologies have also been evaluated for their use in Local versus Wide Area Networks. Our findings suggest that Neural Network techniques are the most common tools used in network traffic volume prediction, followed by linear time series modeling. Backpropagation Neural Networks and Recurrent Neural Networks seem to be able to more accurately predict future network states, perhaps due to their feedback mechanisms, allowing them to serve as memory. At the same time, some authors propose solutions mainly used in image processing (such as the GCN-GAN method) and adapt them to the problems related to traffic prediction. On the other hand, linear time series are mainly used in LAN for short-term traffic prediction. Network traffic volume and blocking probability were the most desired metrics to predict in communication networks. Some papers used traffic prediction to trigger network reconfigurations adaptively. Other uses for traffic prediction include determining ideal periods for software patch downloading and file transfers. As more research is conducted, software-defined networking is pushing high-performing, automated, and intelligent networks into the realm of reality. Machine learning and statistical modules may be loaded onto physical devices attached to the communication network for monitoring, prediction, and configuration. The increasing availability of high-quality historical data may also facilitate these models' faster and more accurate 'learning'. What remains is to determine which methods are most viable in which network scenarios. Various factors in each technique should be considered, such as processing requirement, calculation time, power consumption, and network topology. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Technique** & **Reference** & **Type** & **Local/Wide** & **Metric** & **Application** \\ \hline \multirow{8}{*}{NN} & 17 & NN & Wide & Traffic Volume & Cellular traffic \\  & 19 & LSTM & Wide & Blocking Probability & Optical networks \\  & 20 & GCN-GAN & Wide & Traffic Volume & Communication networks \\  & 21 & RNN & Wide & Blocking Probability & Elastic optical networks \\  & 24 & BPNN & Wide & Traffic Volume & Big data oriented networks \\  & 25 & LSTM & Wide & Traffic Volume & Universal \\  & 30 & NN & Wide & Traffic Volume & Bufferless OBS/OPS networks \\  & 32 & ELM & Wide & Traffic Volume & \\ \hline \multirow{3}{*}{SVM} & 36 & SVM & Wide & Traffic Volume & LTE networks \\  & 37 & SVM & Local & Traffic Volume & Wireless Local Area Networks \\  & 38 & Hybrid SVM & Wide & Traffic Volume & Metro network \\ \hline \multirow{3}{*}{PCA} & 44 & PCA & Wide & Traffic Volume & 17 network backbone \\  & 45 & PCA & Wide & Blocking Probability & Optical networks \\  & 47 & PCA & Local & Traffic Volume & Bluetooth networks \\  & 49 & PCA & Wide & Traffic Volume & Metro networks \\ \hline \multirow{3}{*}{Statistical Model} & 52 & Markov Decision Process & Wide & Blocking Probability & Optical networks \\  & 53 & Bayesian Estimation & Local & Traffic Volume & ONU \\  & 54 & Statistical analysis & Wide & Traffic Volume & Elastic optical networks \\ \hline \multirow{2}{*}{Linear Time Series} & 57 & ARMA & Local & Traffic Volume & TCP traffic \\  & 58 & CARMA & Local & Traffic Volume & MPEG, JPEG, Ethernet, Internet \\ \hline \end{tabular}
\end{table} 
Table 1: List of papers on short-term communication traffic prediction.