summarise_split: Summarise train-test splits

In tscv: Functions and Utilities for Tidy Time Series Forecasting and Time Series Cross-Validation

Summarise train-test splits

Description

Summarise the time and row-index ranges of training and test samples.

Usage

summarise_split(data)

Arguments

data	A valid tsibble in long format. It must contain the columns split, sample, and id.

Details

summarise_split() is intended for data sets that contain sliced training and test observations from a time series cross-validation workflow. The input must be a tsibble with the columns split, sample, and id:

• split: train-test split identifier;

• sample: sample label, usually "train" or "test";

• id: integer row index within the original time series.

The function returns one row per split. For each split, it reports the time range and index range of each sample.

Value

A tibble containing the summarized split ranges.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_stats()

Examples

library(dplyr)
library(tsibble)

context <- list(
  series_id = "bidding_zone",
  value_id = "value",
  index_id = "time"
)

main_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 120)

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 48,
  n_ahead = 24,
  n_skip = 23,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  mutate(sample = "train")

test_frame <- slice_test(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  mutate(sample = "test")

split_data <- bind_rows(train_frame, test_frame) |>
  group_by(bidding_zone, split, sample) |>
  mutate(id = row_number()) |>
  ungroup() |>
  as_tsibble(
    index = time,
    key = c(bidding_zone, split, sample)
  )

summarise_split(split_data)

tscv documentation built on May 13, 2026, 9:07 a.m.