ProbCast: Open-source production, evaluation and visualisation of probabilistic forecasts

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Abstract

Probabilistic forecasts quantify the uncertainty associated with predictions about the future. They are useful in decision-making, and essential when the user’s objective is risk management, or optimisation with asymmetric cost functions. Probabilistic forecasts are widely utilised in finance and weather services, and increasingly by the energy industry, to name a few applications. The R package ProbCast provides a framework for producing probabilistic forecasts using a range of leading predictive models, plus visualisation, and evaluation of the resulting forecasts. It supports both parametric and nonparametric density forecasting, and high-dimensional dependency modelling based on Gaussian Copulas. ProbCast enables a simple workflow for common tasks associated with probabilistic forecasting, making leading methodologies more accessible then ever before. These features are described and then illustrated using an example from energy forecasting, and the first public release of the package itself accompanies this paper.
Original languageEnglish
Title of host publication2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Place of PublicationPiscataway, NJ.
PublisherIEEE
ISBN (Electronic)9781728128221
ISBN (Print)9781728128238
DOIs
Publication statusPublished - 1 Sep 2020
EventPMAPS 2020 - the 16th International Conference on Probabilistic Methods Applied to Power Systems - Online, Liege, Belgium
Duration: 18 Aug 202021 Aug 2020

Conference

ConferencePMAPS 2020 - the 16th International Conference on Probabilistic Methods Applied to Power Systems
CountryBelgium
CityLiege
Period18/08/2021/08/20

Keywords

  • probabilistic forecasting
  • software
  • uncertainty quantification

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