prep_newdata: Prepare a data set for recursive forecasting

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Prepare a data set for recursive forecasting

Description

Prepare a test data for recursive forecasting by appropriately removing existing (actual) values from a specified range of columns (lagged response columns) of the data set. Handles seasonal data with gaps.

Usage

prep_newdata(newdata, recursive_colRange)

Arguments

newdata	Data set to be ared. Should be a tsibble.
recursive_colRange	The range of column numbers (lagged response columns) in newdata from which existing values should be removed. Make sure such columns are positioned together in increasing lag order (i.e. lag_1, lag_2, ..., lag_m, lag_m = maximum lag used) in newdata, with no break in the lagged variable sequence even if some of the intermediate lags are not used as predictors.

Value

A tibble.

smimodel documentation built on April 8, 2026, 5:06 p.m.