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" .


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install.packages("forecastML")

0.5.0 by Nickalus Redell, 11 days ago


https://github.com/nredell/forecastML/


Browse source code at https://github.com/cran/forecastML


Authors: Nickalus Redell


Documentation:   PDF Manual  


Task views: Time Series Analysis


MIT + file LICENSE license


Imports tidyr, dplyr, rlang, magrittr, stringr, lubridate, ggplot2, future.apply, methods, purrr

Suggests glmnet, DT, knitr, rmarkdown, xgboost, randomForest, testthat, covr


See at CRAN