Imports: colorspace, fracdiff, ggplot2 (>= 2.0.0), graphics, lmtest,
Suggests: expsmooth, knitr, rmarkdown, rticles, testthat

Suggests: expsmooth, uroot, knitr, rmarkdown, rticles, testthat,

Title: Convenient Functions for Ensemble Time Series Forecasts
person("David", "Shaub", email = "davidshaub@gmx.com", role = c("aut", "cre")),
person("Peter", "Ellis", email = "peter.ellis2013nz@gmail.com", role = c("ctb"))
Description: Convenient functions for ensemble forecasts in R combining
approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
nnetar(), stlm(), and tbats() can be combined with equal weights or weights based on
in-sample errors. Future methods such as cross validation are planned.

approaches from the 'forecast' package. Forecasts generated from auto.arima(),
ets(), nnetar(), stlm(), and tbats() can be combined with equal weights or
weights based on in-sample errors. Future methods such as cross validation are

person("Peter", "Ellis", email = "peter.ellis2013nz@gmail.com", role = c("aut"))

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.

Imports: doParallel (>= 1.0.10), foreach (>= 1.4.3), ggplot2 (>=

Suggests: GMDH, knitr, rmarkdown, testthat

Suggests: GMDH, knitr, rmarkdown, roxygen2, testthat

Packaged: 2018-05-04 13:36:00 UTC; david.shaub

based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross
validation for time series data with user-supplied models and forecasting
functions is also supported to evaluate model accuracy.

thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights
based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
or cross-validated weights. Cross validation for time series data with user-supplied models
and forecasting functions is also supported to evaluate model accuracy.
Depends: R (>= 3.1.1), forecast (>= 8.1), thief