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

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.

[1]  Pierre Pinson,et al.  Evaluation of wind power forecasts—An up‐to‐date view , 2019, Wind Energy.

[2]  R. Rigby,et al.  Generalized additive models for location, scale and shape , 2005 .

[3]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[4]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[5]  T. Hamill,et al.  Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities* , 2015 .

[6]  P. Gaillard,et al.  Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting , 2016 .

[7]  David McMillan,et al.  Probabilistic access forecasting for improved offshore operations , 2021, International Journal of Forecasting.

[8]  M. Dolores Ugarte,et al.  Statistical Methods for Spatio-temporal Systems , 2006 .

[9]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[10]  Bri-Mathias Hodge,et al.  Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry , 2017 .

[11]  A. Raftery,et al.  Probabilistic forecasts, calibration and sharpness , 2007 .

[12]  J. Abel Guy,et al.  fanplot: An R Package for Visualising Sequential Distributions , 2015 .

[13]  Adrian E. Raftery,et al.  Use and communication of probabilistic forecasts , 2014, Stat. Anal. Data Min..

[14]  R. Weron,et al.  Recent advances in electricity price forecasting: A review of probabilistic forecasting , 2016 .

[15]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[16]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[17]  David McMillan,et al.  Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting , 2020, IEEE Transactions on Sustainable Energy.

[18]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[19]  Raydonal Ospina,et al.  Inflated beta distributions , 2007, 0705.0700.

[20]  Pierre Pinson,et al.  The future of forecasting for renewable energy , 2019, WIREs Energy and Environment.

[21]  Rob J Hyndman,et al.  Density Forecasting for Long-Term Peak Electricity Demand , 2010, IEEE Transactions on Power Systems.

[22]  J. Zico Kolter,et al.  The Multiple Quantile Graphical Model , 2016, NIPS.

[23]  Benjamin Hofner,et al.  Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting , 2012 .

[24]  Alexander Jordan,et al.  Evaluating Probabilistic Forecasts with scoringRules , 2017, Journal of Statistical Software.

[25]  Jan Emil Banning Iversen,et al.  RESGen: Renewable Energy Scenario Generation Platform , 2016 .

[26]  Mark Landry,et al.  Probabilistic gradient boosting machines for GEFCom2014 wind forecasting , 2016 .