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, or CV weights. Cross validation for time series data and user-supplied models and forecasting functions is also supported to evaluate model accuracy.

Version: 2.2.12
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: 2018-05-04
Author: David Shaub [aut, cre], Peter Ellis [aut]
Maintainer: David Shaub <davidshaub at gmx.com>
BugReports: https://github.com/ellisp/forecastHybrid/issues
License: GPL-3
URL: https://github.com/ellisp/forecastHybrid
NeedsCompilation: no
Materials: NEWS
In views: TimeSeries
CRAN checks: forecastHybrid results

Downloads:

Reference manual: forecastHybrid.pdf
Vignettes: Using the "forecastHybrid" package
Package source: forecastHybrid_2.2.12.tar.gz
Windows binaries: r-devel: forecastHybrid_2.2.12.zip, r-release: forecastHybrid_2.2.12.zip, r-oldrel: forecastHybrid_2.2.12.zip
OS X binaries: r-release: forecastHybrid_2.2.12.tgz, r-oldrel: forecastHybrid_2.2.12.tgz
Old sources: forecastHybrid archive

Reverse dependencies:

Reverse imports: mafs, sutteForecastR

Linking:

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