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 + file LICENSE URL: https://github.com/nredell/forecastML/ NeedsCompilation: no Materials: README In views: TimeSeries CRAN checks: forecastML results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=forecastML to link to this page.
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