caretForecast: Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Version:	0.1.1
Depends:	R (≥ 3.6)
Imports:	forecast (≥ 8.15), caret (≥ 6.0.88), magrittr (≥ 2.0.1), methods (≥ 4.1.1), dplyr (≥ 1.0.9), generics (≥ 0.1.3)
Suggests:	Cubist (≥ 0.3.0), knitr (≥ 1.29), testthat (≥ 2.3.2)
Published:	2022-10-24
Author:	Resul Akay [aut, cre]
Maintainer:	Resul Akay <resulakay1 at gmail.com>
BugReports:	https://github.com/Akai01/caretForecast/issues
License:	GPL (≥ 3)
URL:	https://github.com/Akai01/caretForecast
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	caretForecast results

Documentation:

Reference manual:

caretForecast.pdf

Downloads:

Package source:	caretForecast_0.1.1.tar.gz
Windows binaries:	r-devel: caretForecast_0.1.1.zip, r-release: caretForecast_0.1.1.zip, r-oldrel: caretForecast_0.1.1.zip
macOS binaries:	r-release (arm64): caretForecast_0.1.1.tgz, r-oldrel (arm64): caretForecast_0.1.1.tgz, r-release (x86_64): caretForecast_0.1.1.tgz, r-oldrel (x86_64): caretForecast_0.1.1.tgz
Old sources:	caretForecast archive

Reverse dependencies:

Reverse imports:

OptiSembleForecasting, WaveletETS, WaveletGBM, WaveletKNN, WaveletLSTM

Linking:

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