# Time Series Forecasting with Machine Learning Methods

## Documentation for package ‘forecastML’ version 0.9.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

combine_forecasts | Combine multiple horizon-specific forecast models to produce one forecast
create_lagged_df | Create model training and forecasting datasets with lagged, grouped, dynamic, and static features
create_skeleton | Remove the features from a lagged training dataset to reduce memory consumption
create_windows | Create time-contiguous validation datasets for model evaluation
data_buoy | NOAA buoy weather data
data_buoy_gaps | NOAA buoy weather data
data_seatbelts | Road Casualties in Great Britain 1969-84
fill_gaps | Prepare a dataset for modeling by filling in temporal gaps in data collection
plot.forecastML | Plot an object of class 'forecastML'
plot.forecast_error | Plot forecast error
plot.forecast_model_hyper | Plot hyperparameters
plot.forecast_results | Plot an object of class forecast_results
plot.lagged_df | Plot datasets with lagged features
plot.training_results | Plot an object of class training_results
plot.validation_error | Plot validation dataset forecast error
plot.windows | Plot validation datasets
predict.forecast_model | Predict on validation datasets or forecast
return_error | Compute forecast error
return_hyper | Return model hyperparameters across validation datasets
summary.lagged_df | Return a summary of a lagged_df object
train_model | Train a model across horizons and validation datasets