step_slidify_augment: Slidify Rolling Window Transformation (Augmented Version)
In timetk: A Tool Kit for Working with Time Series

View source: R/recipes-step_slidify_augment.R

step_slidify_augment

R Documentation

Slidify Rolling Window Transformation (Augmented Version)

Description

step_slidify_augment creates a a specification of a recipe step that will "augment" (add multiple new columns) that have had a sliding function applied.

Usage

step_slidify_augment(
  recipe,
  ...,
  period,
  .f,
  align = c("center", "left", "right"),
  partial = FALSE,
  prefix = "slidify_",
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  f_name = NULL,
  skip = FALSE,
  id = rand_id("slidify_augment")
)

## S3 method for class 'step_slidify_augment'
tidy(x, ...)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more numeric columns to be smoothed. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`period`	The number of periods to include in the local rolling window. This is effectively the "window size".
`.f`	A summary formula in one of the following formats: `mean` with no arguments `function(x) mean(x, na.rm = TRUE)` `~ mean(.x, na.rm = TRUE)`, it is converted to a function.
`align`	Rolling functions generate `period - 1` fewer values than the incoming vector. Thus, the vector needs to be aligned. Alignment of the vector follows 3 types: Center: `NA` or `.partial` values are divided and added to the beginning and end of the series to "Center" the moving average. This is common for de-noising operations. See also `⁠[smooth_vec()]⁠` for LOESS without NA values. Left: `NA` or `.partial` values are added to the end to shift the series to the Left. Right: `NA` or `.partial` values are added to the beginning to shif the series to the Right. This is common in Financial Applications such as moving average cross-overs.
`partial`	Should the moving window be allowed to return partial (incomplete) windows instead of NA values. Set to FALSE by default, but can be switched to TRUE to remove NA's.
`prefix`	A prefix for generated column names, default to "slidify_".
`role`	For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new variable columns created by the original variables will be used as predictors in a model.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`f_name`	A character string for the function being applied. This field is a placeholder and will be populated during the `tidy()` step.
`skip`	A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations
`id`	A character string that is unique to this step to identify it.
`x`	A `step_slidify_augment` object.

Details

Alignment

Rolling functions generate period - 1 fewer values than the incoming vector. Thus, the vector needs to be aligned. Alignment of the vector follows 3 types:

Center: NA or partial values are divided and added to the beginning and end of the series to "Center" the moving average. This is common for de-noising operations. See also ⁠[smooth_vec()]⁠ for LOESS without NA values.
Left: NA or partial values are added to the end to shift the series to the Left.
Right: NA or partial values are added to the beginning to shif the series to the Right. This is common in Financial Applications such as moving average cross-overs.

Partial Values

The advantage to using partial values vs NA padding is that the series can be filled (good for time-series de-noising operations).
The downside to partial values is that the partials can become less stable at the regions where incomplete windows are used.

If instability is not desirable for de-noising operations, a suitable alternative is step_smooth(), which implements local polynomial regression.

Value

For step_slidify_augment, an updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the selectors or variables selected), value (the feature names).

Examples

# library(tidymodels)
library(dplyr)
library(recipes)
library(parsnip)

m750 <- m4_monthly %>%
    filter(id == "M750") %>%
    mutate(value_2 = value / 2)

m750_splits <- time_series_split(m750, assess = "2 years", cumulative = TRUE)

# Make a recipe
recipe_spec <- recipe(value ~ date + value_2, rsample::training(m750_splits)) %>%
    step_slidify_augment(
        value, value_2,
        period = c(6, 12, 24),
        .f = ~ mean(.x),
        align = "center",
        partial = FALSE
    )

recipe_spec %>% prep() %>% juice()

bake(prep(recipe_spec), rsample::testing(m750_splits))

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