On Feature Selection in Short-Term Prediction of Backbone Optical Network Traffic

The knowledge about future traffic volumes is beneficial for the network operators in many areas. Short-term forecasting of multiple traffic types helps with efficient resource utilization by enabling near real-time adjustment. An important issue is the choice of a suitable prediction model to obtain the most accurate traffic forecasts. A machine learning (ML) algorithm picked for this task can be further tuned by an appropriate feature selection. In this paper, we propose three models containing sets of additional input features to improve the prediction quality of different ML algorithms. We evaluate our models on multiple datasets containing diverse types of network traffic. In extensive numerical experiments, we prove the high prediction quality of ML regression algorithms aided by our proposed additional features. Obtained mean absolute percentage errors (MAPE) are, depending on the predicted traffic type, as little as 1–10%.