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

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.