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.

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Description file content

Package
forecastHybrid
Title
Convenient Functions for Ensemble Time Series Forecasts
Version
1.0.8
Date
2017-07-10
Description
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.
Depends
R (>= 3.1.1), doParallel (>= 1.0.10), forecast (>= 8.1), foreach (>= 1.4.3), ggplot2 (>= 2.2.0)
Imports
reshape2 (>= 1.4.2), zoo (>= 1.7)
Suggests
knitr, rmarkdown, testthat
VignetteBuilder
knitr
License
GPL-3
URL
BugReports
https://github.com/ellisp/forecastHybrid/issues
LazyData
true
RoxygenNote
6.0.1
ByteCompile
true
NeedsCompilation
no
Packaged
2017-07-10 16:41:35 UTC; david
Author
David Shaub [aut, cre], Peter Ellis [aut]
Maintainer
David Shaub
Repository
CRAN
Date/Publication
2017-07-12 10:22:22 UTC

install.packages('forecastHybrid')

1.0.8

a month ago

https://github.com/ellisp/forecastHybrid

David Shaub

GPL-3

Depends on

R (>= 3.1.1), doParallel (>= 1.0.10), forecast (>= 8.1), foreach (>= 1.4.3), ggplot2 (>= 2.2.0)

Imports

reshape2 (>= 1.4.2), zoo (>= 1.7)

Suggests

knitr, rmarkdown, testthat

Discussions