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 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>.
| Version: | 0.7.0 |
| Depends: | R (≥ 3.4.0), dplyr (≥ 0.8.3) |
| 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) |
| 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) |
| Published: | 2020-01-07 |
| Author: | Nickalus Redell |
| Maintainer: | Nickalus Redell <nickalusredell at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/nredell/forecastML/ |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | TimeSeries |
| CRAN checks: | forecastML results |
| Reference manual: | forecastML.pdf |
| Vignettes: |
Customizing Wrapper Functions Forecasting with Multiple Time Series Custom Feature Lags forecastML Overview |
| Package source: | forecastML_0.7.0.tar.gz |
| Windows binaries: | r-devel: forecastML_0.7.0.zip, r-devel-gcc8: forecastML_0.7.0.zip, r-release: forecastML_0.7.0.zip, r-oldrel: forecastML_0.7.0.zip |
| OS X binaries: | r-release: forecastML_0.7.0.tgz, r-oldrel: forecastML_0.7.0.tgz |
| Old sources: | forecastML archive |
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