Description
Forecast Reconciliation.
Description
Classical (bottom-up and top-down), optimal and heuristic combination forecast point (Di Fonzo and Girolimetto, 2023) <doi:10.1016/j.ijforecast.2021.08.004> and probabilistic (Girolimetto et al. 2023) <arXiv:2303.17277> reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly constrained time series.
README.md
FoReco
The FoReco (Forecast Reconciliation) package is designed for forecast reconciliation, a post-forecasting process aimed to improve the accuracy of the base forecasts for a system of linearly constrained (e.g. hierarchical/grouped) time series.
It offers classical (bottom-up and top-down), and modern (optimal and heuristic combination) forecast reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly constrained time series.
The main functions are:
htsrec()
: cross-sectional (contemporaneous) forecast reconciliation.thfrec()
: forecast reconciliation for a single time series through temporal hierarchies.lccrec()
: level conditional forecast reconciliation for genuine hierarchical/grouped time series.tdrec()
: top-down (cross-sectional, temporal, cross-temporal) forecast reconciliation for genuine hierarchical/grouped time series.ctbu()
: bottom-up cross-temporal forecast reconciliation.tcsrec()
: heuristic first-temporal-then-cross-sectional cross-temporal forecast reconciliation.cstrec()
: heuristic first-cross-sectional-then-temporal cross-temporal forecast reconciliation.iterec()
: heuristic iterative cross-temporal forecast reconciliation.octrec()
: optimal combination cross-temporal forecast reconciliation.
Installation
You can install the stable version on R CRAN
install.packages("FoReco")
You can also install the development version from Github
# install.packages("devtools")
devtools::install_github("daniGiro/FoReco")
Links
- Source code: https://github.com/daniGiro/FoReco
- Site documentation: https://danigiro.github.io/FoReco/
Getting help
If you encounter a clear bug, please file a minimal reproducible example on GitHub.