ts_sample: Time Series Sample

In tspredit: Time Series Prediction with Integrated Tuning

Time Series Sample

Description

Split a time-series representation into train and test sets.

Extracts test_size rows from the end (minus an optional offset) as the test set. The remaining initial rows form the training set. The offset is useful to reproduce experiments with different forecast origins.

For sliding-window workflows, the most coherent usage is to materialize the lagged representation first and split it afterwards. This preserves the lag context required by the earliest rows of the test partition, mirroring the package's univariate forecasting examples.

Usage

ts_sample(ts, test_size = 1, offset = 0)

Arguments

ts	A ts_data or ts_data_mv object.
test_size	Integer. Number of rows in the test split (default = 1).
offset	Integer. Offset from the end before the test split (default = 0).

Value

A list with ⁠$train⁠ and ⁠$test⁠ (both ts_data).

Examples

# Setting up a ts_data and making a temporal split
# Load example dataset and build windows
data(tsd)
ts <- ts_data(tsd$y, 10)

# Separating into train and test
test_size <- 3
samp <- ts_sample(ts, test_size)

# First five rows from training data
ts_head(samp$train, 5)

# Last five rows from training data
ts_head(samp$train[-c(1:(nrow(samp$train)-5)),])

# Testing data
ts_head(samp$test)

tspredit documentation built on Sept. 9, 2026, 9:08 a.m.