Defines rolling slices — rolling

Sometimes your validation or testing data has more values than the horizon of your model but you still want to create predictions for each time step on them. rolling_slice only generates the slices and can be useful for debuging purposes.

Usage

rolling_predict(object, past_data, new_data, step = NULL)

rolling_slice(object, past_data, new_data, step = NULL)

Arguments

object: a model object for which prediction is desired.
past_data: A data.frame() with past information for creating the predictions. It should include at least lookback values - but can be more. It's concatenated with new_data before passing forward. If NULL, the data used to train the model is used.
new_data: A data.frame() containing a dataset to generate predictions for. In general it's used to pass static and known information to generate forecasts.
step: Default is the step to be the same as the horizon o the model, that way we have one prediction per slice.

Details

This function will combine your past_data (that can also include your training data) and create slices so you create predictions for each value in new_data.

Functions

rolling_slice: Generate slices for predictions without adding the predictions.