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" .


Reference manual

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0.9.0 by Nickalus Redell, 7 months 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, rlang, magrittr, lubridate, ggplot2, future.apply, methods, purrr, data.table, dtplyr, tibble

Depends on dplyr

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

See at CRAN