Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.
Version: | 1.0.0 |
Depends: | R (≥ 2.10) |
Imports: | torch (≥ 0.11.0), purrr (≥ 1.0.1), imputeTS (≥ 3.3), lubridate (≥ 1.9.2), ggplot2 (≥ 3.5.1), scales (≥ 1.3.0) |
Suggests: | knitr, testthat (≥ 3.0.0) |
Published: | 2025-07-15 |
DOI: | 10.32614/CRAN.package.temper |
Author: | Giancarlo Vercellino [aut, cre, cph] |
Maintainer: | Giancarlo Vercellino <giancarlo.vercellino at gmail.com> |
License: | GPL-3 |
URL: | https://rpubs.com/giancarlo_vercellino/temper |
NeedsCompilation: | no |
Materials: | NEWS |
CRAN checks: | temper results |
Reference manual: | temper.pdf |
Package source: | temper_1.0.0.tar.gz |
Windows binaries: | r-devel: not available, r-release: temper_1.0.0.zip, r-oldrel: not available |
macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): temper_1.0.0.tgz, r-oldrel (x86_64): temper_1.0.0.tgz |
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