split_index: Create indices for train and test splits.

In ahaeusser/tscv: Functions and utilities for tidy time series forecasting and time series cross-validation

Create indices for train and test splits.

Description

The function creates the split indices for train and test samples (i.e. partitioning into time slices) for time series cross-validation. The user can choose between stretch and slide. The first is an expanding window approach, while the latter is a fixed window approach. The user can define the window sizes for training and testing via n_init and n_ahead, as well as the step size for increments via n_step.

Usage

split_index(
  n_total,
  n_init,
  n_ahead,
  n_skip = 0,
  n_lag = 0,
  mode = "slide",
  exceed = FALSE
)

Arguments

n_total	The total number of observations of the time series.
n_init	The number of periods for the initial training window (must be positive).
n_ahead	The forecast horizon (n-steps-ahead, must be positive).
n_skip	The number of periods to skip between windows (must be zero or positive integer).
n_lag	A value to include a lag between the training and testing set. This is useful if lagged predictors will be used during training and testing.
mode	Character value. Define the setup of the training window for time series cross validation. stretch is equivalent to an expanding window approach and slide is a fixed window approach.
exceed	Logical value. If TRUE, out-of-sample splits exceeding the sample size are created.

Value

A list containing the indices for train and test as integer vectors.

ahaeusser/tscv documentation built on July 26, 2023, 3:18 p.m.