# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 4 Neural Networks
### Relevant Papers
On the other hand, the multiplicative seasonal autoregressive integrated moving average (MSARIMA) is a model used for forecasting time series by looking at time-lagged value errors. The smooth part of the time series is input into the ESN for forecasting, while the seasonal part is input into ARIMA for long-term predictability. The hybrid model is used for predictions 6 to 168 h into the future. This study finds that their hybrid model has a 12% improvement in prediction accuracy over other similar methods, echo state networks, SVM, and Wavelet-MSARIMA. Hybrid models, which use different ML techniques to better generate short- and long-term predictions separately before being consolidated, may be a viable solution to predicting seasonal traffic data. A potential obstacle in using hybrid models can be its requirement for data pre-processing to produce two inputs to be used in two ML networks separately. This method may require more time to prepare the data as well as to train both models before consolidating the output prediction. [PERSON] et al. [24] use a Backpropagation Neural Network (BPNN) to predict future links in an elastic optical network (EON) in order to achieve better performance via increasing bandwidth utilization and decreasing blocking probability. The BPNN is used to predict the arrival time and holding time of future connections. These values are called 'future connections' in the paper. Meanwhile, 'assigned connections' are connections in which the arrival time and holding time are known since spectral resources are pre-specified along a particular path. A Minimum Comprehensive Weight Algorithm (MCW) predicts future arrival and holding time values on candidate paths. On the other hand, a Minimum Network Competition Algorithm (MNC) is used to calculate the resource competition for all candidate paths to compare them. The optimal path is the path with the least competition and with the least blocking probability. The MCW and MNC are routing and spectrum assignment (RSA) algorithms with holding time awareness, proposed to achieve lower blocking probability. The study finds that the MCW performs better than other algorithms in elastic optical network performance. [PERSON] et al. [25] use an Artificial Neural Network model to adaptively configure virtual network topologies (VNT) to scale to increased traffic demands automatically. Their methodology, which they call the VNT reconfiguration based on traffic prediction (VENTURE), will analyze real-time-monitored traffic data to predict hour-by-hour traffic. More specifically, the model predicts the maximum bitrate for every origin-destination pair and modifies the VNT to be capable of handling this maximum bitrate. ANN fitting, in this case, is an interactive procedure to find the lowest Akaike information criterion (AIC), where a lower AIC value means higher optimization between the accuracy and number of inputs. Three separate algorithms are then used to determine the pathway for data flows in the VNT. First, flows are allocated for already existing virtual links. Secondly, new virtual links are created and fully capacitated. Lastly, any residual bitrate is served on newly created uncapacitated virtual links. These three algorithms are iterated every hour to recompute and reconfigure the VNT to account for next-hour traffic. [PERSON] et al. find that VENTURE is able to achieve transponder savings between 8% and 42% when compared with traditional threshold-based VNT reconfiguration. By predicting the maximum bitrate hour-by-hour, VENTURE can effectively reduce energy consumption, as it automatically de-activates and re-activates transponders during low- and high-traffic hours. [PERSON] et al. [26] explore the use of a nonlinear autoregressive exogenous model (NARX) to predict long-term chaotic laser and variable bitrate (VBR) video traffic time series data sets. More specifically, they seek to determine the viability of using NARX for long-term data prediction.