Outlier Detection with RegARIMA Model — x13_regarima

Wrapper around rjd3x13::regarima_outliers() to detect Additive Outliers (AO) or Level Shifts (LS) in a seasonally adjusted series.

Usage

x13_regarima_outliers(
  y,
  order = c(0, 1, 1),
  mean = FALSE,
  ao = TRUE,
  ls = TRUE
)

Arguments

y: A time series object (class "ts").
order: An integer vector of length 3 specifying the non-seasonal ARIMA orders \((p, d, q)\).
mean: Logical. If TRUE, the model includes a constant term.
ao, ls: Logical flags indicating whether Additive Outliers (AO) and Level Shifts (LS) should be detected, respectively.

Value

A list with two components:

ao: A vector of dates where Additive Outliers were detected (or NULL if none).
ls: A vector of dates where Level Shifts were detected (or NULL if none).

Details

This function uses an underlying RegARIMA model via rjd3x13 to automatically identify structural breaks and temporary shocks in the input series, returning their locations for downstream robust trend-cycle estimation.

Examples

if (FALSE) { # rjd3jars::check_java_version(silent = TRUE)
x13_regarima_outliers(cars_registrations)
}