Outlier Detection with a RegARIMA Model

Usage

regarima_outliers(
  y,
  order = c(0L, 1L, 1L),
  seasonal = c(0L, 1L, 1L),
  mean = FALSE,
  X = NULL,
  X.td = NULL,
  ao = TRUE,
  ls = TRUE,
  tc = FALSE,
  so = FALSE,
  cv = 0,
  clean = FALSE
)

Arguments

y: the dependent variable (a ts object).
order, seasonal: the orders of the ARIMA model.
mean: Boolean to include or not the mean.
X: user defined regressors (other than calendar).
X.td: calendar regressors.
ao, ls, so, tc: Boolean to indicate which type of outliers should be detected.
cv: numeric. The entered critical value for the outlier detection procedure. If equal to 0 the critical value for the outlier detection procedure is automatically determined by the number of observations.
clean: Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.

Value

a "JD3_REGARIMA_OUTLIERS" object, containing input variables and results

Examples

# estimate model
model <- regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
# print outliers
model$model$variables
#> [1] "AO.220" "AO.219" "AO.277" "LS.400" "LS.280"