R: Time Series Feature (Signature) Generator

step_timeseries_signature {timetk} | R Documentation

## Time Series Feature (Signature) Generator

### Description

`step_timeseries_signature` creates a a specification of a recipe step that will convert date or date-time data into many features that can aid in machine learning with time-series data

### Usage

    step_timeseries_signature(
      recipe,
      ...,
      role = "predictor",
      trained = FALSE,
      columns = NULL,
      skip = FALSE,
      id = rand_id("timeseries_signature")
    )

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

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | One or more selector functions to choose which variables that will be used to create the new variables. The selected variables should have class `Date` or `POSIXct`. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`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_timeseries_signature` object.

### Details

Date Variable Unlike other steps, `step_timeseries_signature` does not remove the original date variables. `recipes::step_rm()` can be used for this purpose.

Scaling index.num The `index.num` feature created has a large magnitude (number of seconds since 1970-01-01). It's a good idea to scale and center this feature (e.g. use `recipes::step_normalize()`).

Removing Unnecessary Features By default, many features are created automatically. Unnecessary features can be removed using `recipes::step_rm()`.

### Value

For `step_timeseries_signature`, 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)

    FB_tbl <- FANG %>% filter(symbol == "FB")

    # Create a recipe object with a timeseries signature step
    rec_obj <- recipe(adjusted ~ ., data = FB_tbl) %>%
        step_timeseries_signature(date)

    # View the recipe object
    rec_obj

    # Prepare the recipe object
    prep(rec_obj)

    # Bake the recipe object - Adds the Time Series Signature
    bake(prep(rec_obj), FB_tbl)

    # Tidy shows which features have been added during the 1st step
    #  in this case, step 1 is the step_timeseries_signature step
    tidy(rec_obj)
    tidy(rec_obj, number = 1)

[Package timetk version 2.5.0 Index]