  Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging
task, particularly when coupled with a degree of mobility. In this work, we
introduce HINDSIGHT++, an open-source R-based framework for bandwidth
forecasting experimentation in MBB networks with Long Short Term Memory (LSTM)
networks. We instrument HINDSIGHT++ following an Automated Machine Learning
(AutoML) paradigm to first, alleviate the burden of data preprocessing, and
second, enhance performance related aspects. We primarily focus on bandwidth
forecasting for Fifth Generation (5G) networks. In particular, we leverage
5Gophers, the first open-source attempt to measure network performance on
operational 5G networks in the US. We further explore the LSTM performance
boundaries on Fourth Generation (4G) commercial settings using NYU-METS, an
open-source dataset comprising of hundreds of bandwidth traces spanning
different mobility scenarios. Our study aims to investigate the impact of
hyperparameter optimization on achieving state-of-the-art performance and
beyond. Results highlight its significance under 5G scenarios showing an
average Mean Absolute Error (MAE) decrease of near 30% when compared to prior
state-of-the-art values. Due to its universal design, we argue that HINDSIGHT++
can serve as a handy software tool for a multitude of applications in other
scientific fields.
