# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 7 Principal Component Analysis
#### Relevant Papers  
As explained by [PERSON] et al. [44], PCA can be applied to the initial data to filter out outliers. This is especially useful during the pre-processing stage, before the data are passed into a machine learning algorithm. Using a network with 529 variables, the authors found that using PCA, 11 principal components were enough to be an accurate representation of the traffic data as they were able to account for 91.1% of the total variability. Figure 7: Scatterplot showing an example of PCA analysis generating two principal components. [PERSON] et al. [45] use an Artificial Neural Network (ANN) with eight input parameters to estimate performance in wavelength-routed optical networks (WRONs). This approach differs from other Neural Network methodologies by including a physical layer input parameter in the Neural Network. The purpose of the methodology is to present a fast and easy means to estimate blocking probability in WRONs given physical impairments. Independent variables related to the physical layer and topological properties of the WRON were determined by a principal component analysis (PCA). The PCA was performed to generalize and identify variables that would make the model easier to train and use across different networks. The best set of parameters was then used to train the ANN, and the results were compared to network results simulated by a discrete event simulator called SIMPTON. SIMPTON was used to simulate networks with different traffic loads of 100 and 200 Erlangs. The results found that the ANN was an effective method to estimate blocking probability in cases where inadequate training and execution time is desired. There are two downsides to this approach. The first is that the same input criteria need to be used to estimate the blocking probability of the network accurately. Secondly, the error increases for networks with lower traffic loads. The authors of [46] performed a comparative experimental analysis with a PCA-based method on a real-life data set and achieved better decomposition performance. In the real-life data set, results showed that, through robust PCA, most of the significant volume anomalies were short-lived and well-isolated in the residual traffic matrix while PCA failed. In traffic flow prediction experiments based on decomposition, the result based on robust PCA outperformed the PCA and simple average. This provides adequate evidence that robust PCA is more appropriate for traffic flow matrix analysis. The authors concluded that a robust PCA shows promising abilities in improving the accuracy and reliability of traffic flow analysis. [PERSON] et al. [47] perform prediction of the time evolution of origin-destination (O-D) matrices using the PCA method. PCA shows that the dimensionality of the time series of O-D demand can be reduced significantly. The authors also discover that the results from the PCA method can be used to reveal the structure in the underlying temporal variability patterns in dynamic O-D matrices. PCA has also been successfully applied to IP networks, as presented in [48]. The proposed system uses seven traffic flow attributes: bits, packets, and number of flows, to detect problems, and source and destination IP addresses and ports, to provide the network administrator with the necessary information to solve them. The authors applied a hybrid PCA method for network traffic data, and the results showed good traffic prediction and encouraging false alarm generation and detection accuracy on the detection schema using thresholds. Finally, the authors of [49] propose a method based on principal component analysis and Support Vector Regression (PCA-SVR) for the short-term simultaneous prediction of network traffic flow that is multidimensional compared with a traditional single point. Data from a typical traffic network of Beijing City, China, are used for the analysis. Other models, such as ANN and ARIMA, are also developed as a comparison of the performance of both these techniques is carried out to show the effectiveness of the novel method.