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(), thetam(), nnetar(), stlm(), and tbats() 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: 3.0.14
Depends: R (≥ 3.1.1), forecast (≥ 8.1)
Imports: doParallel (≥ 1.0.10), foreach (≥ 1.4.3), ggplot2 (≥ 2.2.0), zoo (≥ 1.7)
Suggests: GMDH, knitr, rmarkdown, roxygen2, testthat
Published: 2018-07-22
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_3.0.14.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: forecastHybrid_3.0.14.tgz, r-oldrel: forecastHybrid_3.0.14.tgz
Old sources: forecastHybrid archive

Reverse dependencies:

Reverse imports: mafs, sutteForecastR, TSstudio


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