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
- 2.1.11
- Date
- 2018-03-27
- 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), forecast (>= 8.1),
- Imports
- doParallel (>= 1.0.10), foreach (>= 1.4.3), ggplot2 (>= 2.2.0), reshape2 (>= 1.4.2), zoo (>= 1.7)
- Suggests
- GMDH, knitr, rmarkdown, roxygen2, 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
- 2018-03-27 03:37:45 UTC; dashaub
- Author
- David Shaub [aut, cre], Peter Ellis [aut]
- Maintainer
- David Shaub
- Repository
- CRAN
- Date/Publication
- 2018-03-27 22:30:02 UTC
install.packages('forecastHybrid')
2.1.11
26 days ago
https://github.com/ellisp/forecastHybrid
David Shaub
GPL-3
Depends on
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)