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), forecast(>=8.1),
Imports doParallel(>=1.0.10), foreach(>=1.4.3), ggplot2(>=2.2.0), reshape2(>=1.4.2), zoo(>=1.7)
Suggests knitr, rmarkdown, testthat
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mafs, sutteForecastR
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Package forecastHybrid
Materials
URL https://github.com/ellisp/forecastHybrid
Task Views TimeSeries
Version 1.1.9
Published 2017-08-23
License GPL-3
BugReports https://github.com/ellisp/forecastHybrid/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks forecastHybrid check results
Package source forecastHybrid_1.1.9.tar.gz