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 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: 0.6.0
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: 2019-11-23
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_0.6.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: forecastML_0.6.0.tgz, r-oldrel: forecastML_0.6.0.tgz
Old sources: forecastML archive


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