FoRecoML
Forecast Reconciliation with Machine Learning
Nonlinear forecast reconciliation with machine learning in
cross-sectional (Spiliotis et al. 2021
FoRecoML 
Forecast Reconciliation is a post-forecasting process designed
to improve accuracy and align forecasts within systems of linearly
constrained time series (e.g. hierarchical or grouped). The FoRecoML
package provides nonlinear forecast reconciliation procedures using
Machine Learning in cross-sectional, temporal, and
cross-temporal settings. FoRecoML inherits time series processing
functionalities from FoReco.
The core functions for reconciliation are:
-
csrml()Cross-sectional Reconciliation with Machine Learning -
terml()Temporal Reconciliation with Machine Learning -
ctrml()Cross-temporal Reconciliation with Machine Learning
Machine learning models that can be used with FoRecoML include random
forest (randomForest), extreme gradient boosting (xgboost), light
gradient boosting machine (lightgbm), and models supported by the
mlr3 package.
Installation
You can install the stable version on CRAN
install.packages("FoRecoML")
You can install the development version of FoRecoML from
GitHub
# install.packages("devtools")
devtools::install_github("danigiro/FoRecoML")
Code of Conduct
Please note that the FoRecoML project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.