Package: modeltime.ensemble 1.1.0.9000

Matt Dancho

modeltime.ensemble: Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Authors:Matt Dancho [aut, cre], Business Science [cph]

modeltime.ensemble_1.1.0.9000.tar.gz
modeltime.ensemble_1.1.0.9000.zip(r-4.7)modeltime.ensemble_1.1.0.9000.zip(r-4.6)modeltime.ensemble_1.1.0.9000.zip(r-4.5)
modeltime.ensemble_1.1.0.9000.tgz(r-4.6-any)modeltime.ensemble_1.1.0.9000.tgz(r-4.5-any)
modeltime.ensemble_1.1.0.9000.tar.gz(r-4.7-any)modeltime.ensemble_1.1.0.9000.tar.gz(r-4.6-any)
modeltime.ensemble_1.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
modeltime.ensemble/json (API)
NEWS

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

Bug tracker:https://github.com/business-science/modeltime.ensemble/issues

Pkgdown/docs site:https://business-science.github.io

On CRAN:

Conda:

ensembleensemble-learningforecastforecastingmodeltimestackingstacking-ensembletidymodelstimetime-seriestimeseries

8.87 score 80 stars 1 packages 137 scripts 789 downloads 15 exports 194 dependencies

Last updated from:ace8e091e4. Checks:1 ERROR, 2 OK, 6 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR267
source / vignettesOK295
linux-release-x86_64NOTE289
macos-release-arm64NOTE174
macos-oldrel-arm64NOTE181
windows-develNOTE217
windows-releaseNOTE269
windows-oldrelNOTE204
wasm-releaseOK194

Exports::=.data%>%as_labelas_nameenquoenquosensemble_averageensemble_model_specensemble_nested_averageensemble_nested_weightedensemble_weightedexprsymsyms

Dependencies:abindanytimeaskpassbackportsbase64encBHbigDbitbit64bitopsbroombslibcachemcallrcheckmateclassclicliprclockcodetoolscolorspacecommonmarkconflictedcpp11crayoncrosstalkcurldata.tabledescdiagramdialsDiceDesigndigestdistributionaldoParalleldplyrdygraphsevaluateextraDistrfarverfastmapfontawesomeforcatsforeachforecastfracdifffsfurrrfuturefuture.applyGauProgenericsggplot2glmnetglobalsgluegowergridExtragtgtablehardhathighrhmshtmltoolshtmlwidgetshttrinferinlineipredisobanditeratorsjanitorjquerylibjsonlitejuicyjuiceKernSmoothknitrlabelinglaterlatticelavalazyevallbfgslifecyclelistenvlitedownlmtestloolubridatemagrittrmarkdownMASSMatrixmatrixStatsmemoisemimemixoptmodeldatamodelenvmodeltimemodeltime.resamplenlmennetnumDerivopensslotelpadrparallellyparsnippatchworkpillarpkgbuildpkgconfigplotlyposteriorprettyunitsprocessxprodlimprogressprogressrpromisesprophetpspurrrquadprogquantmodQuickJSRR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppRollreactablereactRreadrrecipesrlangrmarkdownrpartrsamplerstanrstantoolsrstudioapiS7sassscalessfdshapeslidersnakecasesparsevctrssplitfngrSQUAREMStanHeadersstringistringrsurvivalsystailortensorAtibbletictoctidymodelstidyrtidyselecttimechangetimeDatetimetktinytextseriestsfeaturesTTRtunetzdburcautf8V8vctrsviridisLitevroomwarpwithrworkflowsworkflowsetsxfunxgboostxml2xtsyamlyardstickzoo

Autoregressive Forecasting (Recursive Ensembles)

Rendered fromrecursive-ensembles.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2023-12-13
Started: 2021-04-03

Getting Started with Modeltime Ensemble

Rendered fromgetting-started-with-modeltime-ensemble.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2023-12-13
Started: 2020-09-21

Iterative Forecasting with Nested Ensembles

Rendered fromnested-ensembles.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2023-12-13
Started: 2021-10-13