tk_augment_differences: Add many differenced columns to the data

In timetk: A Tool Kit for Working with Time Series

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	One or more column(s) to have a transformation applied. Usage of tidyselect functions (e.g. contains()) can be used to select multiple columns.
.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.

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)

Value

Returns a tibble object describing the timeseries.

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

library(dplyr)

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

timetk documentation built on Nov. 2, 2023, 6:18 p.m.