forecastML: Time Series Forecasting with Machine Learning Methods

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
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:

Reference manual:	forecastML.pdf
Vignettes:	Forecast Combination Customizing Wrapper Functions Direct Forecasting with Multiple Time Series Custom Feature Lags forecastML Overview

Downloads:

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

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