Introduction | Loading the data | Forecasts | Reconciliation | Comparison of results | Comparison of the covariance matrix estimates | References
Package: bayesRecon 1.0.1
Dario Azzimonti
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) <https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) <doi:10.48550/arXiv.2506.19554>.
Authors:
bayesRecon_1.0.1.tar.gz
bayesRecon_1.0.1.zip(r-4.7)bayesRecon_1.0.1.zip(r-4.6)bayesRecon_1.0.1.zip(r-4.5)
bayesRecon_1.0.1.tgz(r-4.6-any)bayesRecon_1.0.1.tgz(r-4.5-any)
bayesRecon_1.0.1.tar.gz(r-4.7-any)bayesRecon_1.0.1.tar.gz(r-4.6-any)
bayesRecon_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bayesRecon/json (API)
| # Install 'bayesRecon' in R: |
| install.packages('bayesRecon', repos = c('https://idsia.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/idsia/bayesrecon/issues
Pkgdown/docs site:https://idsia.github.io
Datasets:
- carparts_example - Example of a time series from carparts
- extr_mkt_events - Extreme market events dataset
- extr_mkt_events_basefc - Base forecasts for the extreme market events dataset
- infantMortality - Infant Mortality grouped time series dataset
- M3_example - Example of a time series from the M3 forecasting competition
- M5_CA1_basefc - Example of hierarchical forecasts for a store from the M5 competition
- swiss_tourism - Swiss tourism: monthly Swiss overnight stay data
Last updated from:92ebfba9fe. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 217 | ||
| source / vignettes | OK | 303 | ||
| linux-release-x86_64 | OK | 188 | ||
| macos-release-arm64 | OK | 157 | ||
| macos-oldrel-arm64 | OK | 193 | ||
| windows-devel | OK | 154 | ||
| windows-release | OK | 149 | ||
| windows-oldrel | OK | 166 | ||
| wasm-release | OK | 150 |
Exports:.check_hierarchical.compute_naive_cov.core_reconc_BUIS.core_reconc_MixCond.core_reconc_t.core_reconc_TDcond.get_Au.get_HG.lowest_levget_reconc_matricesmulti_log_score_optimizationPMF_get_meanPMF_get_quantilePMF_get_varPMF_samplePMF_summaryreconc_BUISreconc_gaussianreconc_MCMCreconc_MixCondreconc_treconc_TDcondschaferStrimmer_covtemporal_aggregation
t-Rec: Reconciliation of Gaussian forecasts by modelling the uncertainty on the covariance matrix
Last update: 2026-04-28
Started: 2026-04-16
Probabilistic Reconciliation via Conditioning with bayesRecon
Introduction | Installation | Temporal hierarchy over a count time series | Temporal hierarchy over a smooth time series | Gaussian reconciliation of a cross-sectional hierarchy | References
Last update: 2026-04-16
Started: 2023-07-26
Reconciliation of M5 hierarchy with mixed-type forecasts
Introduction | Data and base forecasts | Gaussian reconciliation | Reconciliation with mixed-conditioning | Top down conditioning | Comparison | Skill scores | Boxplots | Full reproducibility | References
Last update: 2026-04-16
Started: 2024-05-29
Properties of the reconciled distribution via conditioning
Introduction | Data and base forecasts | Reconciliation via conditioning | Reconciled mean and variance | References
Last update: 2026-03-05
Started: 2023-12-14
