The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
| Version: | 0.9.0 |
| Depends: | R (≥ 3.5.0),dplyr (≥ 0.8.3) |
| Imports: | tidyr (≥ 0.8.1),rlang (≥ 0.4.0),magrittr (≥ 1.5),lubridate (≥ 1.7.4),ggplot2 (≥ 3.1.0),future.apply (≥1.3.0), methods,purrr (≥ 0.3.2),data.table (≥ 1.12.6),dtplyr (≥ 1.0.0),tibble (≥ 2.1.3) |
| Suggests: | glmnet (≥ 2.0.16),DT (≥ 0.5),knitr (≥ 1.22),rmarkdown (≥ 1.12.6),xgboost (≥ 0.82.1),randomForest (≥ 4.6.14),testthat (≥ 2.2.1),covr (≥ 3.3.1) |
| Published: | 2020-05-07 |
| DOI: | 10.32614/CRAN.package.forecastML |
| Author: | Nickalus Redell |
| Maintainer: | Nickalus Redell <nickalusredell at gmail.com> |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/nredell/forecastML/ |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | TimeSeries |
| CRAN checks: | forecastML results[issues need fixing before 2025-12-22] |
| Package source: | forecastML_0.9.0.tar.gz |
| Windows binaries: | r-devel:forecastML_0.9.0.zip, r-release:forecastML_0.9.0.zip, r-oldrel:forecastML_0.9.0.zip |
| macOS binaries: | r-release (arm64):forecastML_0.9.0.tgz, r-oldrel (arm64):forecastML_0.9.0.tgz, r-release (x86_64):forecastML_0.9.0.tgz, r-oldrel (x86_64):forecastML_0.9.0.tgz |
| Old sources: | forecastML archive |
Please use the canonical formhttps://CRAN.R-project.org/package=forecastMLto link to this page.