Addressing Traffic Prediction Uncertainty in Multi-Period Planning Optical Networks

Deep-quantile regression is leveraged to capture traffic prediction uncertainty over future network planning intervals. We show that quantile predictions, acting as discriminative margins, result to significant spectrum savings compared to empirically estimated myopic margins considered. © 2022 The Author(s)