Forecasting Lightpath Quality of Transmission and Implementing Uncertainty in the Forecast Models

Abstract

The recent popularity of using deep learning models for the forecasting of time series calls for methods to not only predict the target but also measure the uncertainty of the prediction accurately. Working with time series requires reliable and stable forecasters. An essential component of the reliability of machine learning (ML) and deep learning (DL) models is the estimation of the uncertainty. In this work, we address building and characterizing time series forecasters, including N-Beats, Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) against the Naive model, and define the confidence margins, and uncertainty for the selected model.All the implementations are conducted in Python programming language. Random sampling is performed to avoid overfitting. Our target field data is North American Service Provider data sets (NASP). Among the implemented models, the MLP model is selected to measure the uncertainty and confidence level, and the Monte Carlo dropout, which approximates Bayesian uncertainty, is applied during inference to render the implementation of uncertainty calculations. Quantile Regression is also implemented on the MLP algorithm as a baseline to predict the confidence intervals and to evaluate our strategy for estimating uncertainty. To establish reliable uncertainty estimation in time series predictions, we performed uncertainty calibration. Motivated by recent developments in Expected Uncertainty Calibration Error (UCE), we modified the uncertainty calculated by the probabilistic Bayesian estimations. Detailed experiments and architectures of the solution are presented.

Non-technological barriers: the last frontier towards AI-powered intelligent optical networks

“…A prime example of legacy issues is the ML-aided QoT estimation 15 – 17 operation in optical networks, which has traditionally been dominated by non-ML approaches such as Gaussian noise (GN) model 18 and its variants 19 . ML-based QoT estimation methods, despite offering significant advantage in scenarios involving certain uncertainties about link parameters values 20 , 21 , have not been successful yet in achieving broad adoption in current fiber-optic networks and it may take a while before these techniques are deemed suitable substitutes for their legacy counterparts.…”

“…A relevant example of lack of standardization and regulatory frameworks is the ML-aided lightpaths’ QoT estimation, where the ML algorithms are trained to learn the complex mapping between the feature vectors, comprising of few selected parameters of the link/signal, and the lightpath’s chosen QoT metric 15 – 17 . However, there is presently no standardization of the used feature vectors and various proposed solutions apply dissimilar parameter sets, leading to divergent QoT estimation performances.…”