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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).
Package details |
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| Author | Dario Azzimonti [aut, cre] (<https://orcid.org/0000-0001-5080-3061>), Nicolò Rubattu [aut] (<https://orcid.org/0000-0002-2703-1005>), Lorenzo Zambon [aut] (<https://orcid.org/0000-0002-8939-993X>), Giorgio Corani [aut] (<https://orcid.org/0000-0002-1541-8384>) |
| Maintainer | Dario Azzimonti <dario.azzimonti@gmail.com> |
| License | LGPL (>= 3) |
| Version | 0.3.1 |
| Package repository | View on CRAN |
| Installation |
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