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.

Version:	1.1.0
Depends:	modeltime (≥ 1.2.3), modeltime.resample (≥ 0.3.0), R (≥ 3.5)
Imports:	tune (≥ 2.0.0), rsample, yardstick, workflows (≥ 0.2.1), recipes (≥ 0.1.15), timetk (≥ 2.5.0), tibble, dplyr (≥ 1.0.0), tidyr, purrr, stringr, rlang (≥ 0.1.2), cli, generics, magrittr, tictoc, parallel, doParallel, foreach, glmnet
Suggests:	gt, dials, utils, earth, testthat, tidymodels, xgboost, lubridate, knitr, rmarkdown
Published:	2025-09-04
DOI:	10.32614/CRAN.package.modeltime.ensemble
Author:	Matt Dancho [aut, cre], Business Science [cph]
Maintainer:	Matt Dancho <mdancho at business-science.io>
BugReports:	https://github.com/business-science/modeltime.ensemble/issues
License:	MIT + file LICENSE
URL:	https://business-science.github.io/modeltime.ensemble/, https://github.com/business-science/modeltime.ensemble
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	modeltime.ensemble results

Documentation:

Reference manual:	modeltime.ensemble.html , modeltime.ensemble.pdf
Vignettes:	Getting Started with Modeltime Ensemble (source, R code) Iterative Forecasting with Nested Ensembles (source, R code) Autoregressive Forecasting (Recursive Ensembles) (source, R code)

Downloads:

Package source:	modeltime.ensemble_1.1.0.tar.gz
Windows binaries:	r-devel: modeltime.ensemble_1.1.0.zip, r-release: modeltime.ensemble_1.1.0.zip, r-oldrel: modeltime.ensemble_1.1.0.zip
macOS binaries:	r-release (arm64): modeltime.ensemble_1.1.0.tgz, r-oldrel (arm64): modeltime.ensemble_1.1.0.tgz, r-release (x86_64): modeltime.ensemble_1.1.0.tgz, r-oldrel (x86_64): modeltime.ensemble_1.1.0.tgz
Old sources:	modeltime.ensemble archive

Reverse dependencies:

Reverse imports:

LLMAgentR

Linking:

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