create_timefolds: Creates Folds for Time Series Data

In splitTools: Tools for Data Splitting

Creates Folds for Time Series Data

Description

This function provides a list with in- and out-of-sample indices per fold used for time series k-fold cross-validation, see Details.

Usage

create_timefolds(y, k = 5L, use_names = TRUE, type = c("extending", "moving"))

Arguments

y	Any vector of the same length as the data intended to split.
k	Number of folds.
use_names	Should folds be named? Default is TRUE.
type	Should in-sample data be "extending" over the folds (default) or consist of one single fold ("moving")?

Details

The data is first partitioned into k+1 sequential blocks B_1 to B_{k+1}. Each fold consists of two index vectors: one with in-sample row numbers, the other with out-of-sample row numbers. The first fold uses B_1 as in-sample and B_2 as out-of-sample data. The second one uses either B_2 (if type = "moving") or \{B_1, B_2\} (if type = "extending") as in-sample, and B_3 as out-of-sample data etc. Finally, the kth fold uses \{B_1, ..., B_k\} ("extending") or B_k ("moving") as in-sample data, and B_{k+1} as out-of-sample data. This makes sure that out-of-sample data always follows in-sample data.

Value

A nested list with in-sample and out-of-sample indices per fold.

See Also

partition(), create_folds()

Examples

y <- runif(100)
create_timefolds(y)
create_timefolds(y, use_names = FALSE)
create_timefolds(y, use_names = FALSE, type = "moving")

splitTools documentation built on June 7, 2023, 6:25 p.m.