# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 4 Neural Networks
### Relevant Papers
NARX differs from an RNN in that the output of the NARX is fed back into its input, while an RNN only has feedback loops within the hidden layer [27]. A NARX network has an exogenous input regressor, which, in this particular approach, contains sample values of the actual time series. Another feedback loop to the input layer is supplied with an output regressor, which contains estimated data points from the Neural Network. These combine to supply long-term information and short-term information, respectively [26], to the system in order to predict future values accurately. The authors find that NARX Neural Networks can be used for long-term prediction and improve prediction performance compared to alternative Neural Network solutions, namely Elmann and Time Delay Neural Network (TDNN) models. This is because the output regressor in NARX networks seems to have the impact of reducing the likelihood of data overfitting [26]. Overfitting is a negative effect of machine learning where accurate training results are produced with a local set of training data, but performance may be inaccurate given actual scenarios and data [28]. This means that the ML model only works well with the training data but cannot accurately reflect relationships or patterns in a production environment. The authors thus propose further studies of NARX usage for long-term prediction with other data sets such as electrical load forecasting or financial time series analysis. [PERSON] et al. [29] propose a power-aware lightpath management (PALM) algorithm to reduce the power consumption of tearing down and re-establishing lightpaths. According to [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], the creation of lightpaths in elastic optical networks (EONs) may account for 15% of all power consumption [30]. A traffic prediction module is attached to the EON, consisting of a Backpropagation Neural Network (BPNN) supplemented by a particle swarm optimization (PSO) algorithm to determine initial input weights. The PSO algorithm works by first initializing a random population of weights for each input. Each particle has a velocity value attached to it and remembers its previous best velocity. With each iteration, the velocity value is updated to move towards a better value given its previous best [31]. This process is iterated until the authors determine an optimal velocity value as input for the BPNN. The PALM algorithm depends on the prediction output from the PSO-BPNN to determine whether to keep a lightpath open or to re-route a traffic request to a particular idle lightpath. Simulated results show that the PSO-BPNN at an 80% utilization threshold for the lightpath has a 31-36% improvement in reduced power consumption compared to the energy-efficient manycast (EEM) algorithm over a month. PALM is successful in significantly reducing power consumption in EONs; however, it comes at the price of an approximately 5% increase in bandwidth blocking ratio [29]. [PERSON] et al. [32] test two algorithms using the extreme machine learning (EML) framework to estimate blocking probability in optical burst switching (OBS) networks. Packet loss may occur in burst switched networks when an intermediate node drops packets because it is not reserved for a particular bandwidth. EML is a type of single-hidden-layer Neural Network where the input weights of the hidden nodes are randomized. The two algorithms are designated I-ELM and Log-I-ELM, where the Log-I-ELM algorithm is trained to map the network conditions to the blocking probability. Both algorithms train by adding new, randomly weighted, hidden nodes with each iteration. The number of hidden nodes where the mean square error is lowest is used to predict the blocking probability over different traffic loads. The results indicate that the Log-I-ELM algorithm is the most accurate in estimating blocking probability in a network. [PERSON] et al. [33] propose a Graph Convolution Network (GCN) and Generative Adversarial Network (GAN) to predict traffic spikes for both the short term and long term.