ProbReco-package: ProbReco: Score Optimal Probabilistic Forecast Reconciliation

In ProbReco: Score Optimal Probabilistic Forecast Reconciliation

ProbReco: Score Optimal Probabilistic Forecast Reconciliation

Description

Training of reconciliation weights for probabilistic forecasts to optimise total energy (or variogram) score using Stochastic Gradient Descent with automatically differentiated gradients. See Panagiotelis, Gamakumara, Athanasopoulos and Hyndman, (2020) https://www.monash.edu/business/ebs/research/publications/ebs/wp26-2020.pdf for a description of the methods.

Details

This package carries out probabilistic forecast reconciliation via score optimisation using the method described by \insertCitewp;textualProbReco. Given incoherent (base) probabilistic forecasts formed over a training data set, the function scoreopt finds linear reconciliation weights that optimise total score \insertCitescoresProbReco over the training data. Currently the energy score and variogram score are implemented. The optimisation is carried out using the Adaptive Moments (Adam) variant of Stochastic Gradient Descent developed by \insertCiteadam;textualProbReco. Tuning parameters for this optimisation can be set using scoreopt.control. The gradients are found using the automatic differentiation libraries of the Stan project \insertCitestanProbReco.

A version of the function that allows for simpler inputs is provided by inscoreopt. Rather than using arguments that are lists of realisations and lists of functions, a matrix of data and a matrix of (point) predictions are the main arguments. This function is less general than scoreopt in two ways. First, there are only a limited range of options for producing base forecasts (either from Gaussian distributions or bootstrapping). Second, the scores are evaluated using in-sample predictions rather than genuine forecasts.

Author(s)

Maintainer: Anastasios Panagiotelis [e-mail] (ORCID)

References

\insertAllCited

See Also

Useful links:

• https://github.com/anastasiospanagiotelis/ProbReco

• Report bugs at https://github.com/anastasiospanagiotelis/ProbReco/issues

ProbReco documentation built on April 5, 2023, 5:10 p.m.