forecastML: Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct 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: 0.7.0
Depends: R (≥ 3.4.0), 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)
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: 2020-01-07
Author: Nickalus Redell
Maintainer: Nickalus Redell <nickalusredell at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: forecastML results


Reference manual: forecastML.pdf
Vignettes: Customizing Wrapper Functions
Forecasting with Multiple Time Series
Custom Feature Lags
forecastML Overview
Package source: forecastML_0.7.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: forecastML_0.7.0.tgz, r-oldrel: forecastML_0.7.0.tgz
Old sources: forecastML archive


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