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.

Install the latest version of this package by entering the following in R:
install.packages("forecastHybrid")
AuthorDavid Shaub [aut, cre], Peter Ellis [aut]
Date of publication2017-03-31 06:12:43 UTC
MaintainerDavid Shaub <davidshaub@gmx.com>
LicenseGPL-3
Version0.4.0
https://github.com/ellisp/forecastHybrid

View on CRAN

Functions

accuracy Man page
accuracy.cvts Man page
accuracy.default Man page
accuracy.hybridModel Man page
cvts Man page
extractForecasts Man page
fitted.hybridModel Man page
forecast.hybridModel Man page
forecast.thetam Man page
hybridModel Man page
is.hybridModel Man page
plot.hybridModel Man page
plot.thetam Man page
print.hybridModel Man page
residuals.hybridModel Man page
summary.hybridModel Man page
thetam Man page
tsCombine Man page
tsPartition Man page
tsSubsetWithIndices Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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