Runs all relevant functions for ETS modelling
ETS(
y,
u = NULL,
model = "???",
s = frequency(y),
h = 2 * s,
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
)
An object of class ETS
. See ETSmodel
.
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).
a matrix of input time series. If
the output wanted to be forecast, matrix u
should contain future values for inputs.
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
seasonal period of time series (1 for annual, 4 for quarterly, ...)
forecast horizon. If the model includes inputs h is not used, the lenght of u is used instead.
information criterion for identification ("aic", "bic" or "aicc").
Box-Cox lambda parameter (NULL: estimate)
check for arma models for error component (TRUE / FALSE).
run all models to identify the best one (TRUE / FALSE)
estimate forecasting intervals (TRUE / FALSE)
use bootstrap simulation for predictive distributions
number of simulation runs for bootstrap simulation of predictive distributions
intermediate estimation output (TRUE / FALSE)
constraints limits for alpha parameter
constraints limits for beta parameter
constraints limits for gamma parameter
constraints limits for phi parameter
initial values for parameter search (alpha, beta, phi, gamma) with consraints:
0 < alpha < 1
0 < beta < alpha
0 < phi < 1
0 < gamma < 1 - alpha
Diego J. Pedregal
See help of ETSmodel
.
ETSmodel
, ETSvalidate
,
ETScomponents
, ETSestim
if (FALSE) {
y <- log(AirPAssengers)
m1 <- ETS(y)
m1 <- ETS(y, model = "MAM")
}
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