Use the 'GluonTS' deep learning library inside of 'modeltime'. Available models include 'DeepAR', 'N-BEATS', and 'N-BEATS' Ensemble. Refer to "GluonTS - Probabilistic Time Series Modeling" (<https://ts.gluon.ai/index.html>).
| Version: | 0.1.0 |
| Depends: | modeltime (≥ 0.3.1) |
| Imports: | parsnip, timetk, magrittr, rlang (≥ 0.1.2), reticulate, tibble, forcats, dplyr, tidyr, purrr, stringr, glue, fs |
| Suggests: | tidyverse, tidymodels, knitr, rmarkdown, roxygen2, testthat |
| Published: | 2020-11-30 |
| Author: | Matt Dancho [aut, cre], Business Science [cph] |
| Maintainer: | Matt Dancho <mdancho at business-science.io> |
| BugReports: | https://github.com/business-science/modeltime.gluonts/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/business-science/modeltime.gluonts |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | modeltime.gluonts results |
| Reference manual: | modeltime.gluonts.pdf |
| Vignettes: |
Getting Started with Modeltime GluonTS |
| Package source: | modeltime.gluonts_0.1.0.tar.gz |
| Windows binaries: | r-devel: modeltime.gluonts_0.1.0.zip, r-release: modeltime.gluonts_0.1.0.zip, r-oldrel: modeltime.gluonts_0.1.0.zip |
| macOS binaries: | r-release: modeltime.gluonts_0.1.0.tgz, r-oldrel: modeltime.gluonts_0.1.0.tgz |
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