UComp (version 5.1)

TETS: TETS

Description

Runs all relevant functions for TETS modelling

Usage

TETS(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = 2 * s,
  criterion = "aicc",
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(0, 1),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999,
  Ymin = -Inf,
  Ymax = Inf
)

Value

An object of class TETS. See TETSforecast.

Arguments

y

a time series to forecast (it may be either a numerical vector or a time series object). This is the only input required. If a vector, the additional input s should be supplied compulsorily (see below).

u

a matrix of input time series. If the output wanted to be forecast, matrix u should contain future values for inputs.

model

the model to estimate. It is a single string indicating the type of model for each component with one or two letters:

• Error: ? / A

• Trend: ? / N / A / Ad

• Seasonal: ? / N / A

s

seasonal period of time series (1 for annual, 4 for quarterly, ...)

h

forecast horizon. If the model includes inputs h is not used, the lenght of u is used instead.

criterion

information criterion for identification ("aic", "bic" or "aicc").

forIntervals

estimate forecasting intervals (TRUE / FALSE)

bootstrap

use bootstrap simulation for predictive distributions

nSimul

number of simulation runs for bootstrap simulation of predictive distributions

verbose

intermediate estimation output (TRUE / FALSE)

alphaL

constraints limits for alpha parameter

betaL

constraints limits for beta parameter

gammaL

constraints limits for gamma parameter

phiL

constraints limits for phi parameter

p0

initial values for parameter search (alpha, beta, phi, gamma, sigma2) with consraints:

Ymin

scalar or vector of time varying censoring values from below

Ymax

scalar or vector of time varying censoring values from above

• 0 < alpha < 1

• 0 < beta < alpha

• 0 < phi < 1

• 0 < gamma < 1 - alpha

• sigma2 > 0

Author

Diego J. Pedregal

Details

See help of TETSforecast.

See Also

TETSforecast, TETSvalidate, TETScomponents, TETSestim

Examples

if (FALSE) {
y <- log(AirPAssengers)
m1 <- TETS(y)
m1 <- TETS(y, model = "MAM")
}