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" .


Reference manual

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0.5.0 by Nickalus Redell, 11 days ago


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