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. Refer to papers such as "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.

Version: 0.4.0
Depends: modeltime (≥ 0.5.1), modeltime.resample (≥ 0.1.0), R (≥ 3.5)
Imports: tune (≥ 0.1.2), rsample, yardstick, workflows (≥ 0.2.1), parsnip (≥ 0.1.4), recipes (≥ 0.1.15), dials, timetk (≥ 2.5.0), tibble, dplyr (≥ 1.0.0), tidyr, purrr, glue, stringr, rlang (≥ 0.1.2), cli, crayon, utils, generics, magrittr, glmnet, progressr, tictoc
Suggests: roxygen2, earth, testthat, tidymodels, xgboost, tidyverse, lubridate, knitr, rmarkdown, covr, remotes
Published: 2021-04-05
Author: Matt Dancho [aut, cre], Business Science [cph]
Maintainer: Matt Dancho <mdancho at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: modeltime.ensemble results


Reference manual: modeltime.ensemble.pdf
Vignettes: Getting Started with Modeltime Ensemble
Autoregressive Forecasting (Recursive Ensembles)
Package source: modeltime.ensemble_0.4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: modeltime.ensemble_0.4.0.tgz, r-oldrel: modeltime.ensemble_0.3.0.tgz
Old sources: modeltime.ensemble archive

Reverse dependencies:

Reverse imports: healthyR.ts


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