timetk (version 2.2.1)

tk_augment_differences: Add many differenced columns to the data

Description

A handy function for adding multiple lagged difference values to a data frame. Works with dplyr groups too.

Usage

tk_augment_differences(
  .data,
  .value,
  .lags = 1,
  .differences = 1,
  .log = FALSE,
  .names = "auto"
)

Arguments

.data

A tibble.

.value

A column to have a difference transformation applied

.lags

One or more lags for the difference(s)

.differences

The number of differences to apply.

.log

If TRUE, applies log-differences.

.names

A vector of names for the new columns. Must be of same length as the number of output columns. Use "auto" to automatically rename the columns.

Value

Returns a tibble object describing the timeseries.

Details

Benefits

This is a scalable function that is:

• Designed to work with grouped data using dplyr::group_by()

• Add multiple differences by adding a sequence of differences using the .lags argument (e.g. lags = 1:20)

See Also

Augment Operations:

• tk_augment_timeseries_signature() - Group-wise augmentation of timestamp features

• tk_augment_holiday_signature() - Group-wise augmentation of holiday features

• tk_augment_slidify() - Group-wise augmentation of rolling functions

• tk_augment_lags() - Group-wise augmentation of lagged data

• tk_augment_differences() - Group-wise augmentation of differenced data

• tk_augment_fourier() - Group-wise augmentation of fourier series

Underlying Function:

• diff_vec() - Underlying function that powers tk_augment_differences()

Examples

# NOT RUN {
library(tidyverse)
library(timetk)

m4_monthly %>%
    group_by(id) %>%
    tk_augment_differences(value, .lags = 1:20)

# }