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.

c(
person("David", "Shaub", email = "davidshaub@gmx.com", role = c("aut", "cre")),
person("Peter", "Ellis", email = "peter.ellis2013nz@gmail.com", role = c("aut"))
)
David Shaub [aut, cre],
Peter Ellis [aut]

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.

GMDH, knitr, rmarkdown, roxygen2, testthat

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 (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.

Convenient functions for ensemble forecasts in R combining
approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
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.

Time Series Forecasting with Machine Learning Methods
The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
'fill_gaps.R' 'create_windows.R' 'lagged_df.R'
'return_error.R' 'return_hyper.R' 'train_model.R'
'data_seatbelts.R' 'data_buoy.R' 'data_buoy_gaps.R'