forecastHybrid: Convenient Functions for Ensemble Time Series Forecasts

Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.

Version: 5.0.19
Depends: R (≥ 3.1.1), forecast (≥ 8.12), thief
Imports: doParallel (≥ 1.0.10), foreach (≥ 1.4.3), ggplot2 (≥ 2.2.0), purrr (≥ 0.2.5), zoo (≥ 1.7)
Suggests: GMDH, knitr, rmarkdown, roxygen2, testthat
Published: 2020-08-28
DOI: 10.32614/CRAN.package.forecastHybrid
Author: David Shaub [aut, cre], Peter Ellis [aut]
Maintainer: David Shaub <davidshaub at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
In views: TimeSeries
CRAN checks: forecastHybrid results


Reference manual: forecastHybrid.pdf
Vignettes: Using the "forecastHybrid" package


Package source: forecastHybrid_5.0.19.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): forecastHybrid_5.0.19.tgz, r-oldrel (arm64): forecastHybrid_5.0.19.tgz, r-release (x86_64): forecastHybrid_5.0.19.tgz, r-oldrel (x86_64): forecastHybrid_5.0.19.tgz
Old sources: forecastHybrid archive

Reverse dependencies:

Reverse imports: TSstudio


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