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

Browell, Jethro and Gilbert, Ciaran; (2020) ProbCast : Open-source production, evaluation and visualisation of probabilistic forecasts. In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, BEL. ISBN 9781728128221

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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.

    Creators(s): Browell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X and Gilbert, Ciaran ORCID logoORCID: https://orcid.org/0000-0001-6114-7880;
    Item type:Book Section
    ID code:72822
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:probabilistic forecasting, software, uncertainty quantification, Electrical engineering. Electronics Nuclear engineering, Computer software, Statistics, Probability and Uncertainty, Software, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Science > Mathematics > Computer software
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:18 Jun 2020 13:52
    Last modified:20 Jan 2021 16:23
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72822
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