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.
nnetarobjects in the ensemble. This should address one aspect of incorrect prediction intervals (e.g. issue #37).
f" in the
models =argument for
hybridModel()) and are indeed part of the default - so by default, hybridModel() will now fit six models
accuracy.cvts()is now exported
ggplot2graphics when the argument
ggplot = TRUEis passed.
weights = "cv.errors"in
weights = "insample.errors"and one or more component models perfectly fit the time series
xregis included in
nnetarmodel is not included in the model list
print.hybridModel()to three digits for cleaner display
verboseargument and enable by default in
hybridModel()to display fitting/cross validation progress
weights = "cv.errors"
2 * frequency(y) >= length(y)
not()function from "testthat" package
2 * frequency(y) >= length(y),
weights = "cv.errors")