tfarima (version 0.4.1)

decomp.ssm: Unobserved components decomposition

Description

Estimates the unobserved components of a time series (trend, seasonal, cycle, stationary, and irregular) based on the structure of an underlying model. The estimation can be carried out through the UCARIMA representation, the state-space model (SSM) form, or via forward/backward forecasts.

Usage

# S3 method for ssm
decomp(mdl, tol = 1e-05, ...)
# S3 method for tfm
decomp(
  mdl,
  y = NULL,
  method = c("mixed", "forecast", "backcast"),
  envir = NULL,
  ...
)
# S3 method for ucarima
decomp(mdl, ...)
decomp(mdl, ...)
# S3 method for um
decomp(
  mdl,
  z = NULL,
  method = c("ucarima", "ucarima0", "ssm", "ssm0", "mixed", "forecast", "backcast"),
  envir = parent.frame(),
  ...
)
# S3 method for um
decomp(
  mdl,
  z = NULL,
  method = c("ucarima", "ucarima0", "ssm", "ssm0", "mixed", "forecast", "backcast"),
  envir = parent.frame(),
  ...
)

Value

A data.frame with the estimated unobserved components.

Arguments

mdl

An object of class um or tfm, representing a univariate ARIMA or transfer function model.

tol

numeric tolerance for classifying eigenvalues.

...

Additional arguments passed to internal methods.

y

an object of class ts.

method

Character string specifying the decomposition method. Options are:

• "ucarima", "ucarima0" – using the UCARIMA representation, without or with the canonical requirement.

• "ssm", "ssm0" – using the state-space model representation, with multiple sources of error (MSOE) or a single source of error (SSOE), respectively.

• "mixed" – combining forward and backward forecasts.

• "forecast", "backcast" – using the forward or backward eventual forecast function. The last three options are deprecated and will be removed in a future release.

envir

environment in which the function arguments are evaluated. If NULL the calling environment of this function will be used.

z

an object of class ts.

Details

The function applies the corresponding internal routines to estimate the components depending on the chosen method. For UCARIMA-based methods, the Wiener–Kolmogorov filter is used. For state-space approaches, a Kalman smoother is applied.

Examples

Z <- AirPassengers
um1 <- um(Z, i = list(1, c(1, 12)), ma = list(1, c(1, 12)), bc = TRUE)
uc1 <- decomp(um1, method = "ucarima")