ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts

被引:9
作者
Browell, Jethro [1 ]
Gilbert, Ciaran [1 ]
机构
[1] Univ Strathclyde, Elect & Elect Engn, Glasgow, Lanark, Scotland
来源
2020 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS) | 2020年
基金
英国工程与自然科学研究理事会;
关键词
Probabilistic Forecasting; Software; Uncertainty Quantification; PROPER SCORING RULES;
D O I
10.1109/pmaps47429.2020.9183441
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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 non-parametric 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.
引用
收藏
页数:6
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