ETSmodel: ETSmodel

In UComp: Automatic Unobserved Components and Other Time Series Models

ETSmodel

Description

Estimates and forecasts ETS general univariate models

Usage

ETSmodel(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = max(2 * s, 6),
  criterion = "aicc",
  lambda = 1,
  armaIdent = FALSE,
  identAll = FALSE,
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(1e-08, 1 - 1e-08),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999
)

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 / M Trend: ? / N / A / Ad / M / Md Seasonal: ? / N / A / M
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").
lambda	Box-Cox lambda parameter (NULL: estimate)
armaIdent	check for arma models for error component (TRUE / FALSE).
identAll	run all models to identify the best one (TRUE / FALSE)
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) with consraints: 0 < alpha < 1 0 < beta < alpha 0 < phi < 1 0 < gamma < 1 - alpha

Details

ETSmodel is a function for modelling and forecasting univariate time series with ExponenTial Smoothing (ETS) time series models. It sets up the model with a number of control variables that govern the way the rest of functions in the package will work. It also estimates the model parameters by Maximum Likelihood and forecasts the data.

Value

An object of class ETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ETS object as specified in what follows (function ETS fills in all of them at once):

After running ETSmodel or ETSestim:

p	Estimated parameters
criteria	Values for estimation criteria (LogLik, AIC, BIC, AICc)
yFor	Forecasted values of output
yForV	Variance of forecasted values of output
ySimul	Bootstrap simulations for forecasting distribution evaluation

After running ETSvalidate:

table	Estimation and validation table
comp	Estimated components in matrix form

After running ETScomponents:

comp	Estimated components in matrix form

Author(s)

Diego J. Pedregal

See Also

ETS, ETSvalidate, ETScomponents, ETSestim

Examples

## Not run: 
y <- log(AirPAssengers)
m1 <- ETSmodel(y)
m1 <- ETSmodel(y, model = "A?A")

## End(Not run)

UComp documentation built on May 31, 2023, 7:41 p.m.