'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' 'zzz.R'

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 numeric or factor/sequence 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' 'combine_forecasts.R'
'lagged_df.R' 'return_error.R' 'return_hyper.R' 'train_model.R'
'data_seatbelts.R' 'data_buoy.R' 'data_buoy_gaps.R' 'zzz.R'

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence 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' 'create_skeleton.R'
'combine_forecasts.R' 'lagged_df.R' 'return_error.R'
'return_hyper.R' 'train_model.R' 'data_seatbelts.R'
'data_buoy.R' 'data_buoy_gaps.R' 'zzz.R'

Forecasting for Stationary and Non-Stationary Time Series
c(person("Tobias", "Kley", email = "t.kley@lse.ac.uk", role = c("aut", "cre")),
person("Philip", "Preuss", email = "philip.preuss@rub.de", role = c("aut")),
person("Piotr", "Fryzlewicz", email = "p.fryzlewicz@lse.ac.uk", role = c("aut")))
Methods to compute linear h-step prediction coefficients based
on localised and iterated Yule-Walker estimates and empirical mean square
prediction errors for the resulting predictors.
Tobias Kley [aut, cre],
Philip Preuss [aut],
Piotr Fryzlewicz [aut]