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#' Clean Outliers and Missing Data for Time Series
#'
#' `step_ts_clean` creates a *specification* of a recipe
#' step that will clean outliers and impute time series data.
#'
#' @inheritParams step_ts_impute
#'
#' @return 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) and `value` (the
#' lambda estimate).
#'
#' @details
#'
#' The `step_ts_clean()` function is designed specifically to handle time series
#' using seasonal outlier detection methods implemented in the Forecast R Package.
#'
#' __Cleaning Outliers__
#'
#' #' Outliers are replaced with missing values using the following methods:
#'
#' 1. Non-Seasonal (`period = 1`): Uses `stats::supsmu()`
#' 2. Seasonal (`period > 1`): Uses `forecast::mstl()` with `robust = TRUE` (robust STL decomposition)
#' for seasonal series.
#'
#'
#' __Imputation using Linear Interpolation__
#'
#' Three circumstances cause strictly linear interpolation:
#'
#' 1. __Period is 1:__ With `period = 1`, a seasonality cannot be interpreted and therefore linear is used.
#' 2. __Number of Non-Missing Values is less than 2-Periods__: Insufficient values exist to detect seasonality.
#' 3. __Number of Total Values is less than 3-Periods__: Insufficient values exist to detect seasonality.
#'
#' __Seasonal Imputation using Linear Interpolation__
#'
#' For seasonal series with `period > 1`, a robust Seasonal Trend Loess (STL) decomposition is first computed.
#' Then a linear interpolation is applied to the seasonally adjusted data, and
#' the seasonal component is added back.
#'
#' __Box Cox Transformation__
#'
#' In many circumstances, a Box Cox transformation can help. Especially if the series is multiplicative
#' meaning the variance grows exponentially. A Box Cox transformation can be automated by setting `lambda = "auto"`
#' or can be specified by setting `lambda = numeric value`.
#'
#' @seealso
#' 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()]
#'
#' @references
#' - [Forecast R Package](https://github.com/robjhyndman/forecast)
#' - [Forecasting Principles & Practices: Dealing with missing values and outliers](https://otexts.com/fpp2/missing-outliers.html)
#'
#' @examples
#'
#' library(dplyr)
#' library(tidyr)
#' library(recipes)
#'
#' # Get missing values
#' FANG_wide <- FANG %>%
#' select(symbol, date, adjusted) %>%
#' pivot_wider(names_from = symbol, values_from = adjusted) %>%
#' pad_by_time()
#'
#' FANG_wide
#'
#' # Apply Imputation
#' recipe_box_cox <- recipe(~ ., data = FANG_wide) %>%
#' step_ts_clean(FB, AMZN, NFLX, GOOG, period = 252) %>%
#' prep()
#'
#' recipe_box_cox %>% bake(FANG_wide)
#'
#' # Lambda parameter used during imputation process
#' recipe_box_cox %>% tidy(1)
#'
#'
#'
#' @export
step_ts_clean <-
function(recipe,
...,
period = 1,
lambda = "auto",
role = NA,
trained = FALSE,
lambdas_trained = NULL,
skip = FALSE,
id = rand_id("ts_clean")) {
recipes::add_step(
recipe,
step_ts_clean_new(
terms = recipes::ellipse_check(...),
role = role,
trained = trained,
period = period,
lambda = lambda,
lambdas_trained = lambdas_trained,
skip = skip,
id = id
)
)
}
step_ts_clean_new <-
function(terms, role, trained, period, lambda, lambdas_trained, skip, id) {
recipes::step(
subclass = "ts_clean",
terms = terms,
role = role,
trained = trained,
period = period,
lambda = lambda,
lambdas_trained = lambdas_trained,
skip = skip,
id = id
)
}
#' @export
prep.step_ts_clean <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, data = training, info = info)
recipes::check_type(training[, col_names], types = c("double", "integer"))
# Lambda Calculation
if (is.null(x$lambda[1])) {
lambda_values <- rep(NA, length(col_names))
names(lambda_values) <- col_names
} else if (x$lambda[1] == "auto") {
lambda_values <- training[, col_names] %>%
purrr::map(auto_lambda)
} else {
lambda_values <- rep(x$lambda[1], length(col_names))
names(lambda_values) <- col_names
}
step_ts_clean_new(
terms = x$terms,
role = x$role,
trained = TRUE,
period = x$period,
lambda = x$lambda,
lambdas_trained = lambda_values,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_ts_clean <- function(object, new_data, ...) {
col_names <- names(object$lambdas_trained)
for (i in seq_along(object$lambdas_trained)) {
# Handle "non-numeric" naming issue
val_i <- object$lambdas_trained[i]
if (!is.na(val_i)) {
val_i <- as.numeric(val_i)
}
new_data[, col_names[i]] <- ts_clean_vec(
x = new_data %>% purrr::pluck(col_names[i]),
period = object$period[1],
lambda = val_i
)
}
tibble::as_tibble(new_data)
}
#' @export
print.step_ts_clean <- function(x, width = max(20, options()$width - 35), ...) {
title <- "Time Series Outlier Cleaning on "
recipes::print_step(names(x$lambdas_trained), x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_ts_clean
#' @param x A `step_ts_clean` object.
#' @export
tidy.step_ts_clean <- function(x, ...) {
if (is_trained(x)) {
res <- tibble::tibble(
terms = names(x$lambdas_trained),
lambda = as.numeric(x$lambdas_trained)
)
} else {
term_names <- recipes::sel2char(x$terms)
res <- tibble::tibble(
terms = term_names,
lambda = rlang::na_dbl
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_ts_clean <- function(x, ...) {
c("timetk")
}
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