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Slice test data from a split frame

Source: R/slice_data.R
slice_test.Rd

Extract test observations from a complete time series data set according to a split plan created by make_split().

Usage

slice_test(main_frame, split_frame, context)

Arguments

main_frame

A tibble containing the complete time series data.

split_frame

A tibble containing train and test indices, usually created by make_split().

context

A named list with the identifiers for series_id, value_id, and index_id.

Value

A tibble containing the sliced test data. It contains the same columns as main_frame, plus a split column.

Details

slice_test() uses the row positions stored in the test list-column of split_frame to extract the corresponding observations from main_frame. The function is designed for rolling-origin time series cross-validation workflows.

The returned data has the same columns as main_frame, plus a split column identifying the train-test split. If main_frame contains multiple time series, slicing is performed separately for each series using the series identifier supplied in context.

When make_split() was called with n_lag > 0, the test data may include lagged observations before the forecast horizon.

See also

Other time series cross-validation: make_future(), make_split(), make_tsibble(), slice_train(), split_index()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series == "M23100")

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

test_frame <- slice_test(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
)

test_frame
#> # A tibble: 36 × 5
#>       index series category    value split
#>       <mth> <chr>  <chr>       <dbl> <int>
#>  1 2013 Jan M23100 Demographic  8940     1
#>  2 2013 Feb M23100 Demographic  8950     1
#>  3 2013 Mrz M23100 Demographic  9080     1
#>  4 2013 Apr M23100 Demographic  9270     1
#>  5 2013 Mai M23100 Demographic  9570     1
#>  6 2013 Jun M23100 Demographic  9610     1
#>  7 2013 Jul M23100 Demographic  9510     1
#>  8 2013 Aug M23100 Demographic  9550     1
#>  9 2013 Sep M23100 Demographic  9400     1
#> 10 2013 Okt M23100 Demographic  9310     1
#> # ℹ 26 more rows