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) tools:::Rd_expr_doi("10.1007/978-3-030-67664-3_13") , MCMC reconciliation of count time series (Corani et al., 2024) tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.04.003") , Bottom-Up Importance Sampling (Zambon et al., 2024) tools:::Rd_expr_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
To learn more about bayesRecon, start with the vignettes: browseVignettes(package = "bayesRecon")
The package implements reconciliation via conditioning for probabilistic forecasts of hierarchical time series. The main functions are:
reconc_gaussian(): reconciliation via conditioning of multivariate Gaussian base forecasts; this is done analytically;reconc_BUIS(): reconciliation via conditioning of any probabilistic forecast via importance sampling; this is the recommended option for non-Gaussian base forecasts;reconc_MCMC(): reconciliation via conditioning of discrete probabilistic forecasts via Markov Chain Monte Carlo;reconc_MixCond(): reconciliation via conditioning of mixed hierarchies, where the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions;reconc_TDcond(): reconciliation via top-down conditioning of mixed hierarchies, where the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions.temporal_aggregation(): temporal aggregation of a given time series object of class ts ;get_reconc_matrices(): aggregation and summing matrices for a temporal hierarchy of time series from user-selected list of aggregation levels;schaferStrimmer_cov(): computes the Schäfer-Strimmer shrinkage estimator for the covariance matrix;PMF.get_mean(), PMF.get_var(), PMF.get_quantile(), PMF.summary(), PMF.sample(): functions for handling PMF objects.Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule. ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. tools:::Rd_expr_doi("10.1007/978-3-030-67664-3_13") .
Corani, G., Azzimonti, D., Rubattu, N. (2024). Probabilistic reconciliation of count time series. International Journal of Forecasting 40 (2), 457-469. tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.04.003") .
Zambon, L., Azzimonti, D. & Corani, G. (2024). Efficient probabilistic reconciliation of forecasts for real-valued and count time series. Statistics and Computing 34 (1), 21. tools:::Rd_expr_doi("10.1007/s11222-023-10343-y") .
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.12.004") .
Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). Probabilistic reconciliation of mixed-type hierarchical time series. The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
Useful links:
Maintainer : Dario Azzimonti dario.azzimonti@gmail.com (ORCID)
Authors: