make_split: Create a split_frame for train and test splits per time...

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

Create a split_frame for train and test splits per time series.

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

make_split(
  main_frame,
  context,
  type,
  value,
  n_ahead,
  n_skip = 0,
  n_lag = 0,
  mode = "slide",
  exceed = TRUE
)

Arguments

main_frame	A tibble containing the time series data.
context	A named list with the identifiers for seried_id, value_id and index_id.
type	The type for the initial split. Possible values are "first", "last", "prob".
value	Numeric value specifying the split.
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.