Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted).
Version: 0.3.2 Depends: R (≥ 4.1.0) Imports: stats, utils, lpSolve (≥ 5.6.18) Suggests: knitr, rmarkdown, forecast, glarma, scoringRules, testthat (≥ 3.0.0) Published: 2024-11-04 DOI: 10.32614/CRAN.package.bayesRecon Author: Dario Azzimonti [aut, cre], Nicolò Rubattu [aut], Lorenzo Zambon [aut], Giorgio Corani [aut] Maintainer: Dario Azzimonti <dario.azzimonti at gmail.com> BugReports: https://github.com/IDSIA/bayesRecon/issues License: LGPL (≥ 3) URL: https://github.com/IDSIA/bayesRecon NeedsCompilation: no Materials: README NEWS In views: TimeSeries CRAN checks: bayesRecon results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bayesRecon to link to this page.
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