Package: FoRecoML 1.0.0.9000

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.

Authors:Daniele Girolimetto [aut, cre], Yangzhuoran Fin Yang [aut], Jeroen Rombouts [aut], Ines Wilms [aut]

FoRecoML_1.0.0.9000.tar.gz
FoRecoML_1.0.0.9000.zip(r-4.7)FoRecoML_1.0.0.9000.zip(r-4.6)FoRecoML_1.0.0.9000.zip(r-4.5)
FoRecoML_1.0.0.9000.tgz(r-4.6-any)FoRecoML_1.0.0.9000.tgz(r-4.5-any)
FoRecoML_1.0.0.9000.tar.gz(r-4.7-any)FoRecoML_1.0.0.9000.tar.gz(r-4.6-any)
FoRecoML_1.0.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
FoRecoML/json (API)
NEWS

# Install 'FoRecoML' in R:
install.packages('FoRecoML', repos = c('https://danigiro.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/danigiro/forecoml/issues

Pkgdown/docs site:https://danigiro.github.io

On CRAN:

Conda:

forecastingmachine-learningreconciliationtime-series

3.88 score 1 stars 1 scripts 490 downloads 7 exports 46 dependencies

Last updated from:b835bd0678. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE154
source / vignettesOK215
linux-release-x86_64NOTE157
macos-release-arm64NOTE128
macos-oldrel-arm64NOTE108
windows-develNOTE130
windows-releaseNOTE117
windows-oldrelNOTE125
wasm-releaseOK128

Exports:csrmlcsrml_fitctrmlctrml_fitextract_reconciled_mltermlterml_fit

Dependencies:backportsbbotkcheckmateclicodetoolsdata.tabledigestdistributionalevaluateFoRecofuturefuture.applygenericsglobalsgluejsonlitelatticelgrlifecyclelightgbmlistenvMatrixmiraimlbenchmlr3mlr3learnersmlr3measuresmlr3miscmlr3tuningnanonextnumDerivosqppalmerpenguinsparadoxparallellypillarPRROCR6randomForestRcpprlangS7utf8uuidvctrsxgboost