FoRecoML: Forecast Reconciliation with Machine Learning

Nonlinear forecast reconciliation with machine learning in cross-sectional (Spiliotis et al. 2021 doi:10.1016/j.asoc.2021.107756 ), temporal, and cross-temporal (Rombouts et al. 2024 doi:10.1016/j.ijforecast.2024.05.008 ) frameworks.

See also

Useful links:

• https://github.com/danigiro/FoRecoML

• https://danigiro.github.io/FoRecoML/

• Report bugs at https://github.com/danigiro/FoRecoML/issues

Author

Maintainer: Daniele Girolimetto daniele.girolimetto@unipd.it (ORCID)

Authors:

• Yangzhuoran Fin Yang yangyangzhuoran@gmail.com (ORCID)

• Jeroen Rombouts rombouts@essec.edu (ORCID)

• Ines Wilms i.wilms@maastrichtuniversity.nl (ORCID)