Gaussian Process Regression Ensemble Model for Network Traffic Prediction

Network traffic prediction is substantial for network optimization and resource management. However, designing an efficient predictive model considering different traffic characteristics, including periodicity, nonlinearity, and nonstationarity, is challenging. Recently, ensemble learning is attracting much attention from researchers in the machine learning community. Although ensemble learning has proven exceptional performance in modelling the intricate problems, it may not be able to handle varying patterns and chaotic behaviour, which are typical properties of traffic data (and many other time-series problems). For this reason, ensemble methods show a limited prediction accuracy in network traffic modelling. We address this issue by proposing an ensemble of learners for time-series prediction that considers the accuracy of individual learners as well as diversity among their outcomes. Each learner contributes to the optimization process by finding the optimal accuracy-diversity balance in a segment of feature space. This divide-and-conquer approach avoids complicated objective functions with many local optimums while fitting the ensemble model on large datasets. Experimental results on the real traffic traces show our proposed method outperforms other state-of-the-art predictors with 12% on average in prediction accuracy for different datasets.