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: | 1.0.2 |
| 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 |
| Published: | 2022-10-18 |
| 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 |
| Reference manual: | modeltime.ensemble.pdf |
| Vignettes: |
Getting Started with Modeltime Ensemble Iterative Forecasting with Nested Ensembles Autoregressive Forecasting (Recursive Ensembles) |
| Package source: | modeltime.ensemble_1.0.2.tar.gz |
| Windows binaries: | r-devel: modeltime.ensemble_1.0.2.zip, r-release: modeltime.ensemble_1.0.2.zip, r-oldrel: modeltime.ensemble_1.0.2.zip |
| macOS binaries: | r-release (arm64): modeltime.ensemble_1.0.2.tgz, r-oldrel (arm64): modeltime.ensemble_1.0.2.tgz, r-release (x86_64): modeltime.ensemble_1.0.2.tgz, r-oldrel (x86_64): modeltime.ensemble_1.0.2.tgz |
| Old sources: | modeltime.ensemble archive |
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