Package: modeltime.resample 0.3.0.9000

Matt Dancho

modeltime.resample: Resampling Tools for Time Series Forecasting

A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

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

modeltime.resample_0.3.0.9000.tar.gz
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modeltime.resample_0.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
modeltime.resample/json (API)

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

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

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

Datasets:

On CRAN:

Conda:

accuracy-metricsbacktestingbootstrapbootstrappingcross-validationforecastingmodeltimemodeltime-resampleresamplingstatisticstidymodelstime-series

7.57 score 22 stars 2 packages 47 scripts 648 downloads 20 exports 192 dependencies

Last updated from:27637d4a40. Checks:1 ERROR, 2 OK, 6 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR284
source / vignettesOK360
linux-release-x86_64NOTE293
macos-release-arm64NOTE207
macos-oldrel-arm64NOTE279
windows-develNOTE197
windows-releaseNOTE270
windows-oldrelNOTE197
wasm-releaseOK197

Exports::=.data%>%as_labelas_nameenquoenquosexprget_target_text_from_resamplesmdl_time_fit_resamplesmodeltime_fit_resamplesmodeltime_resample_accuracyplot_modeltime_resamplesplot_time_series_cv_plansymsymstime_series_cvtime_series_splittk_time_series_cv_planunnest_modeltime_resamples

Dependencies:abindanytimeaskpassbackportsbase64encBHbigDbitbit64bitopsbroombslibcachemcallrcheckmateclassclicliprclockcodetoolscolorspacecommonmarkconflictedcpp11crayoncrosstalkcurldata.tabledescdiagramdialsDiceDesigndigestdistributionaldoParalleldplyrdygraphsevaluateextraDistrfarverfastmapfontawesomeforcatsforeachforecastfracdifffsfurrrfuturefuture.applyGauProgenericsggplot2globalsgluegowergridExtragtgtablehardhathighrhmshtmltoolshtmlwidgetshttrinferinlineipredisobanditeratorsjanitorjquerylibjsonlitejuicyjuiceKernSmoothknitrlabelinglaterlatticelavalazyevallbfgslifecyclelistenvlitedownlmtestloolubridatemagrittrmarkdownMASSMatrixmatrixStatsmemoisemimemixoptmodeldatamodelenvmodeltimenlmennetnumDerivopensslotelpadrparallellyparsnippatchworkpillarpkgbuildpkgconfigplotlyposteriorprettyunitsprocessxprodlimprogressprogressrpromisesprophetpspurrrquadprogquantmodQuickJSRR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppRollreactablereactRreadrrecipesrlangrmarkdownrpartrsamplerstanrstantoolsrstudioapiS7sassscalessfdshapeslidersnakecasesparsevctrssplitfngrSQUAREMStanHeadersstringistringrsurvivalsystailortensorAtibbletictoctidymodelstidyrtidyselecttimechangetimeDatetimetktinytextseriestsfeaturesTTRtunetzdburcautf8V8vctrsviridisLitevroomwarpwithrworkflowsworkflowsetsxfunxgboostxml2xtsyamlyardstickzoo

Resampling Panel Data
Panel Data Tutorial Overview | Libraries | Data | Data Preparation | Modeling | Recipe | Models | Prophet | XGBoost | Prophet Boost | Organize in a Modeltime Table | Assess a Single Resample Split | Quantifying Prediction Error Over Time | Apply Models to Resamples | Evaluate Resample Accuracy | Resample Accuracy Plot | Resample Accuracy Table | Model Selection | Wrapup

Last update: 2025-09-03
Started: 2020-10-17

Getting Started with Modeltime Resample
Single Time Series | Getting Started Setup | Step 1 - Make a Cross-Validation Training Plan | Step 2 - Make a Modeltime Table | Step 3 - Generate Resample Predictions | Step 4 - Evaluate the Results | Accuracy Plot | Accuracy Table | Wrapup

Last update: 2024-01-04
Started: 2020-10-14