Package: FoRecoML 1.1.1.9000

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_1.1.1.9000.tar.gz
FoRecoML_1.1.1.9000.zip(r-4.7-any)FoRecoML_1.1.1.9000.zip(r-4.6-any)FoRecoML_1.1.1.9000.zip(r-4.5-any)
FoRecoML_1.1.1.9000.tgz(r-4.6-any)FoRecoML_1.1.1.9000.tgz(r-4.5-any)
FoRecoML_1.1.1.9000.tar.gz(r-4.7-any)FoRecoML_1.1.1.9000.tar.gz(r-4.6-any)
FoRecoML_1.1.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
FoRecoML/json (API)

# 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

3.85 score 1 stars 1 scripts 290 downloads 7 exports 50 dependencies

Last updated from:4829fe92ac. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK159
source / vignettesOK175
linux-releaseOK160
macos-releaseOK112
macos-oldrelOK93
windows-develOK104
windows-releaseOK94
windows-oldrelOK93
wasm-releaseOK101

Exports:csrmlcsrml_fitctrmlctrml_fitextract_reconciled_mltermlterml_fit

Dependencies:backportsbbotkcheckmateclicodetoolsdata.tabledigestdistributionalevaluateFoRecofuturefuture.applygenericsglobalsgluejsonlitelatticelgrlifecyclelightgbmlistenvMatrixmatrixStatsmiraimlbenchmlr3mlr3learnersmlr3measuresmlr3miscmlr3tuningmoocorenanonextnumDerivosqppalmerpenguinsparadoxparallellypillarPRROCR6randomForestrbibutilsRcppRdpackrlangS7utf8uuidvctrsxgboost