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.

Documentation

Manual: forecastHybrid.pdf
Vignette: Using the "forecastHybrid" package

Maintainer: David Shaub <davidshaub at gmx.com>

Author(s): David Shaub*, Peter Ellis*

Install package and any missing dependencies by running this line in your R console:

install.packages("forecastHybrid")

Depends R (>= 3.1.1), ggplot2(>=2.2.0), forecast(>=8.1)
Imports reshape2(>=1.4.2), zoo(>=1.7)
Suggests knitr, rmarkdown, testthat
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Package forecastHybrid
Materials
URL https://github.com/ellisp/forecastHybrid
Task Views TimeSeries
Version 0.4.1
Published 2017-06-18
License GPL-3
BugReports https://github.com/ellisp/forecastHybrid/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks forecastHybrid check results
Package source forecastHybrid_0.4.1.tar.gz