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: 0.3.0
Depends: modeltime (≥ 0.3.0), modeltime.resample (≥ 0.1.0), R (≥ 3.5)
Imports: tune, rsample, yardstick, workflows, parsnip, recipes, 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, testthat, tidymodels, xgboost, tidyverse, lubridate, knitr, rmarkdown, covr, remotes
Published: 2020-11-06
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://github.com/business-science/modeltime.ensemble
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: modeltime.ensemble results

Downloads:

Reference manual: modeltime.ensemble.pdf
Vignettes: getting-started-with-modeltime-ensemble
Package source: modeltime.ensemble_0.3.0.tar.gz
Windows binaries: r-devel: modeltime.ensemble_0.3.0.zip, r-release: modeltime.ensemble_0.3.0.zip, r-oldrel: modeltime.ensemble_0.3.0.zip
macOS binaries: r-release: modeltime.ensemble_0.3.0.tgz, r-oldrel: modeltime.ensemble_0.3.0.tgz
Old sources: modeltime.ensemble archive

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