Time Series Forecasting with Machine Learning Methods

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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