Package: bayesRecon 0.3.1

bayesRecon: Probabilistic Reconciliation via Conditioning

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).

Authors:Dario Azzimonti [aut, cre], Nicolò Rubattu [aut], Lorenzo Zambon [aut], Giorgio Corani [aut]

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13 exports 0.36 score 1 dependencies 31 scripts 1.0k downloads

Last updated 20 days agofrom:e9d90fa7a9. Checks:OK: 7. Indexed: yes.

Exports:get_reconc_matricesPMF.get_meanPMF.get_quantilePMF.get_varPMF.samplePMF.summaryreconc_BUISreconc_gaussianreconc_MCMCreconc_MixCondreconc_TDcondschaferStrimmer_covtemporal_aggregation

Dependencies:lpSolve