Abstract Accepted by: Aris Syntetos Accurate forecasts of daily Coronavirus-2019 (COVID-19) admissions are critical for healthcare planners and decision-makers to better manage scarce resources during around infection peaks. Numerous studies have focused on forecasting COVID-19 at the national or global levels. Localized predictions vital, as they allow resource planning redistribution, but als...

Problem definition: We study the estimation of probability distribution individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients’ overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring underlying uncertainty. Communicating only a forecast to patients b...

Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation techniques could complement numerical prediction, such as nowcasting, monthly projection and extreme event identification. forecasting, the predictive uncertainty arises mainly from data model uncertainties. Current deep learning methods parametric by random sampling parame...

Electricity forecasting has important implications for the key decisions in modern electricity systems, ranging from power generation, transmission, distribution and so on. In literature, traditional statistic approaches, machine-learning methods deep learning (e.g., recurrent neural network) based models are utilized to model trends patterns time-series data. However, they restricted either by...

Gaussian processes are a state-of-the-art method for learning models from data. Data with an underlying periodic structure appears in many areas, e.g., in climatology or robotics. It is often important to predict the long-term evolution of such a time series, and to take the inherent periodicity explicitly into account. In a Gaussian process, periodicity can be accounted for by an appropriate k...

Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24-hour or 48-hour photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, an multi-ahead prediction Multi-L...