forecastML (0.8.0)

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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" .

Maintainer: Nickalus Redell
Author(s): Nickalus Redell

License: MIT + file LICENSE

Uses: data.table, dplyr, dtplyr, future.apply, ggplot2, lubridate, magrittr, purrr, rlang, tibble, tidyr, randomForest, glmnet, testthat, knitr, xgboost, rmarkdown, covr, DT

Released 30 days ago.

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