investigates a methodology for traffic prediction based on multidimensional OD traffic, using Principal Components Analysis as a technique for dimensionality reduction and Local Linear Regression as a technique for trend analysis

The growing demand for link bandwidth and node capacity is a frequent phenomenon in IP network backbones. Within this context traffic prediction is essential for the network operator. Traffic prediction can be undertaken based on link traffic or on origin-destination (OD) traffic which presents better results. This work investigates a methodology for traffic prediction based on multidimensional OD traffic, focusing on the stage of short-term traffic prediction using Principal Components Analysis as a technique for dimensionality reduction and a Local Linear Model based on K-means as a technique for prediction and trend analysis. The results validated with data on a real network present a satisfactory margin of error for its use in practical situations.