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., 2022) tools:::Rd_expr_doi("10.48550/arXiv.2207.09322"), Bottom-Up Importance Sampling (Zambon et al., 2022) tools:::Rd_expr_doi("10.48550/arXiv.2210.02286").
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(): analytical reconciliation of Gaussian base forecasts;
reconc_BUIS(): reconciliation of any probabilistic base forecast via importance sampling;
this is the recommended option for non-Gaussian base forecasts;
reconc_MCMC(): reconciliation of probabilistic discrete base forecasts via Markov Chain Monte Carlo.
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.
Maintainer: Dario Azzimonti dario.azzimonti@gmail.com (ORCID)
Authors:
Nicolò Rubattu nicolo.rubattu@idsia.ch (ORCID)
Lorenzo Zambon lorenzo.zambon@idsia.ch (ORCID)
Giorgio Corani giorgio.corani@idsia.ch (ORCID)
Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. tools:::Rd_expr_doi("10.1007/978-3-030-67664-3_13").
Corani, G., Azzimonti, D., Rubattu, N. (2023). Probabilistic reconciliation of count time series. 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. tools:::Rd_expr_doi("10.1007/s11222-023-10343-y").
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). Properties of the reconciled distributions for Gaussian and count forecasts. tools:::Rd_expr_doi("10.48550/arXiv.2303.15135").