Package: modeltime.ensemble
Type: Package
Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
Version: 1.0.2
Authors@R: c(
    person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
    person("Business Science", role = "cph")
    )
Description: 
    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>.
URL: https://github.com/business-science/modeltime.ensemble
BugReports: https://github.com/business-science/modeltime.ensemble/issues
License: MIT + file LICENSE
Encoding: UTF-8
Depends: modeltime (>= 1.2.3), modeltime.resample (>= 0.2.1), R (>=
        3.5)
Imports: tune (>= 0.1.2), rsample, yardstick, workflows (>= 0.2.1),
        parsnip (>= 0.1.6), recipes (>= 0.1.15), timetk (>= 2.5.0),
        tibble, dplyr (>= 1.0.0), tidyr, purrr, glue, stringr, rlang
        (>= 0.1.2), cli, generics, magrittr, tictoc, parallel,
        doParallel, foreach,
Suggests: gt, crayon, dials, glmnet, progressr, utils, roxygen2, earth,
        testthat, tidymodels, xgboost, tidyverse, lubridate, knitr,
        rmarkdown, covr, qpdf, remotes
RoxygenNote: 7.2.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-10-18 17:35:20 UTC; mdancho
Author: Matt Dancho [aut, cre],
  Business Science [cph]
Maintainer: Matt Dancho <mdancho@business-science.io>
Repository: CRAN
Date/Publication: 2022-10-18 23:02:40 UTC
Built: R 4.2.0; ; 2022-10-19 14:03:15 UTC; unix
