step_ts_pad creates a a specification of a recipe
step that will analyze a Date or Date-time column adding rows
at a specified interval.
step_ts_pad( recipe, ..., by = "day", pad_value = NA, role = "predictor", trained = FALSE, columns = NULL, skip = FALSE, id = rand_id("ts_padding") ) ## S3 method for class 'step_ts_pad' tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
A single column with class
Either "auto", a time-based frequency like "year", "month", "day", "hour", etc, or a time expression like "5 min", or "7 days". See Details.
Fills in padded values. Default is
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.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of variables that will be
used as inputs. This field is a placeholder and will be
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.
A character string that is unique to this step to identify it.
Only one date or date-time variable may be supplied.
step_ts_pad()) does not remove the original date variables.
Interval Specification (by)
Padding can be applied in the following ways:
The eight intervals in are: year, quarter, month, week, day, hour, min, and sec.
Intervals like 30 minutes, 1 hours, 14 days are possible.
Imputing Missing Values
pad_value defaults to
NA, which typically requires imputation.
Some common strategies include:
Numeric data: The
step_ts_impute() preprocessing step can be used to impute
numeric time series data with or without seasonality
Nominal data: The
step_mode_impute() preprocessing step can be used to replace
missing values with the most common value.
step_ts_pad, an updated version of recipe with
the new step added to the sequence of existing steps (if any).
tidy method, a tibble with columns
(the selectors or variables selected),
value (the feature
Padding & Imputation:
Pad Time Series:
Impute missing values with these:
Time Series Analysis:
Diffs & Lags
Main Recipe Functions:
library(recipes) library(dplyr) library(timetk) FB_tbl <- FANG %>% filter(symbol == "FB") %>% select(symbol, date, adjusted) rec_obj <- recipe(adjusted ~ ., data = FB_tbl) %>% step_ts_pad(date, by = "day", pad_value = NA) # View the recipe object rec_obj # Prepare the recipe object prep(rec_obj) # Bake the recipe object - Adds the padding 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(prep(rec_obj)) tidy(prep(rec_obj), number = 1)
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