R: Slidify Rolling Window Transformation

step_slidify {timetk} | R Documentation

## Slidify Rolling Window Transformation

### Description

`step_slidify` creates a a specification of a recipe step that will apply a function to one or more a Numeric column(s).

### Usage

    step_slidify(
      recipe,
      ...,
      period,
      .f,
      align = c("center", "left", "right"),
      partial = FALSE,
      names = NULL,
      role = "predictor",
      trained = FALSE,
      columns = NULL,
      f_name = NULL,
      skip = FALSE,
      id = rand_id("slidify")
    )

    ## S3 method for class 'step_slidify'
    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.
`names` | An optional character string that is the same length of the number of terms selected by `terms`. These will be the names of the new columns created by the step. If `NULL`, existing columns are transformed. If not `NULL`, new columns will be created.
`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 variables that will be used as inputs. This field is a placeholder and will be populated once `recipes::prep()` is used.
`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` 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`, 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).

### See Also

Time Series Analysis:

- Engineered Features: `step_timeseries_signature()`, `step_holiday_signature()`, `step_fourier()`

- Diffs & Lags `step_diff()`, `recipes::step_lag()`

- Smoothing: `step_slidify()`, `step_smooth()`

- Variance Reduction: `step_box_cox()`

- Imputation: `step_ts_impute()`, `step_ts_clean()`

- Padding: `step_ts_pad()`

Main Recipe Functions:

- `recipes::recipe()`

- `recipes::prep()`

- `recipes::bake()`

### Examples

    library(recipes)
    library(tidyverse)
    library(tidyquant)
    library(timetk)

    # Training Data
    FB_tbl <- FANG %>%
        filter(symbol == "FB") %>%
        select(symbol, date, adjusted)

    # New Data - Make some fake new data next 90 time stamps
    new_data <- FB_tbl %>%
        tail(90) %>%
        mutate(date = date %>% tk_make_future_timeseries(length_out = 90))

    # OVERWRITE EXISTING COLUMNS -----

    # Create a recipe object with a step_slidify
    rec_ma_50 <- recipe(adjusted ~ ., data = FB_tbl) %>%
        step_slidify(adjusted, period = 50, .f = ~ AVERAGE(.x))

    # Bake the recipe object - Applies the Moving Average Transformation
    training_data_baked <- bake(prep(rec_ma_50), FB_tbl)

    # Apply to New Data
    new_data_baked <- bake(prep(rec_ma_50), new_data)

    # Visualize effect
    training_data_baked %>%
        ggplot(aes(date, adjusted)) +
        geom_line() +
        geom_line(color = "red", data = new_data_baked)

    # ---- NEW COLUMNS ----
    # Use the `names` argument to create new columns instead of overwriting existing

    rec_ma_30_names <- recipe(adjusted ~ ., data = FB_tbl) %>%
        step_slidify(adjusted, period = 30, .f = AVERAGE, names = "adjusted_ma_30")

    bake(prep(rec_ma_30_names), FB_tbl) %>%
        ggplot(aes(date, adjusted)) +
        geom_line(alpha = 0.5) +
        geom_line(aes(y = adjusted_ma_30), color = "red", size = 1)

[Package timetk version 2.6.1 Index]