Deep learning and optimisation for quality of service modelling

Abstract

Machine learning is increasingly used to create digital twins for data collected from various underlying engineering processes. Such digital twins can be used in a wide variety of activities such as optimisation, forecasting of future data, etc. In this respect, forecasting the evolution of time-series data in the future time-steps is often encountered in various engineering systems and applications. In particular, probabilistic forecasting of time-series data over point-based predictions is often encouraged, but challenging to achieve though. In this work, deep learning (DR) technology is combined with various state-of-the-art mathematical optimisation algorithms in order to effectively achieve the ’confidence-based’ probabilistic predictions of Quality of Service (QoS) data emanating from various low-powered Internet of Things (IoT) devices. The results demonstrate that Deep Neural Networks (DNN), if combined with right mathematical optimisation algorithm, can help generating accurate probabilistic forecasts for both single time-series and a combination of multiple time-series data.