Package: forecastML
Type: Package
Title: Time Series Forecasting with Machine Learning Methods
Version: 0.9.0
Author: Nickalus Redell
Maintainer: Nickalus Redell <nickalusredell@gmail.com>
Description: 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>.
License: MIT + file LICENSE
URL: https://github.com/nredell/forecastML/
Encoding: UTF-8
LazyData: true
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)
RoxygenNote: 7.1.0
Collate: 'fill_gaps.R' 'create_windows.R' 'create_skeleton.R'
        'combine_forecasts.R' 'lagged_df.R' 'return_error.R'
        'return_hyper.R' 'train_model.R' 'data_seatbelts.R'
        'data_buoy.R' 'data_buoy_gaps.R' 'zzz.R'
Depends: R (>= 3.5.0), dplyr (>= 0.8.3)
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)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-05-06 04:46:53 UTC; nickr
Repository: CRAN
Date/Publication: 2020-05-07 15:10:17 UTC
Built: R 4.1.3; ; 2023-04-17 18:00:56 UTC; windows
