Outlier Detection with a Tramo Model

Tramo is a particular regarima model estimation algorithm, mainly used to linearized the series before performing a decomposition with Seats

Usage

tramo_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,
  ml = FALSE,
  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 outliers' detection procedure. If equal to 0 the critical value for the outliers' detection procedure is automatically determined by the number of observations.

ml

Use of maximum likelihood (otherwise approximation by means of Hannan-Rissanen).

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.

Examples

if (FALSE) { # current_java_version >= minimal_java_version
tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
}