Package: forecastML
Type: Package
Title: Time Series Forecasting with Machine Learning Methods
Version: 0.9.1
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'
    'reconcile_forecasts.R'
    'calculate_intervals.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),
    reticulate (>= 1.15)
VignetteBuilder: knitr