auto.ssarima(data, orders = list(ar = c(3, 3), i = c(2, 1), ma = c(3, 3)),
lags = c(1, frequency(data)), combine = FALSE, workFast = TRUE,
initial = c("backcasting", "optimal"), ic = c("AICc", "AIC", "BIC"),
cfType = c("MSE", "MAE", "HAM", "MLSTFE", "MSTFE", "MSEh"), h = 10,
holdout = FALSE, intervals = c("none", "parametric", "semiparametric",
"nonparametric"), level = 0.95, intermittent = c("none", "auto", "fixed",
"croston", "tsb", "sba"), bounds = c("admissible", "none"),
silent = c("none", "all", "graph", "legend", "output"), xreg = NULL,
xregDo = c("use", "select"), initialX = NULL, updateX = FALSE,
persistenceX = NULL, transitionX = NULL, ...)ar, i and ma. If a variable is not provided in the
list, then it is assumed to be equal to zero. At least one variable should
have the same length as lags.lags must correspond to the length of either
ar.orders or i.orders or ma.orders. There is no
restrictions on the length of lags vector.TRUE, then resulting ARIMA is combined using AIC
weights.TRUE, then some of the orders of ARIMA are
skipped. This is not advised for models with lags greater than 12."optimal", meaning that the initial
states are optimised, or "backcasting", meaning that the initials are
produced using backcasting procedure.cfType can
be: MSE (Mean Squared Error), MAE (Mean Absolute Error),
HAM (Half Absolute Moment), MLSTFE - Mean Log Squared Trace
Forecast Error, MSTFE - Mean Squared Trace Forecast Error and
MSEh - optimisation using only h-steps ahead error. If
cfType!="MSE", then likelihood and model selection is done based on
equivalent MSE. Model selection in this cases becomes not optimal.There are also available analytical approximations for multistep functions:
aMSEh, aMSTFE and aMLSTFE. These can be useful in cases
of small samples.
TRUE, holdout sample of size h is taken from
the end of the data.none, aka n - do not produce prediction
intervals.
parametric, p - use state-space structure of ETS. In
case of mixed models this is done using simulations, which may take longer
time than for the pure additive and pure multiplicative models.
semiparametric, sp - intervals based on covariance
matrix of 1 to h steps ahead errors and assumption of normal / log-normal
distribution (depending on error type).
nonparametric, np - intervals based on values from a
quantile regression on error matrix (see Taylor and Bunn, 1999). The model
used in this process is e[j] = a j^b, where j=1,..,h.
The parameter also accepts TRUE and FALSE. Former means that
parametric intervals are constructed, while latter is equivalent to
none.
none, meaning that the data should be considered as non-intermittent;
2. fixed, taking into account constant Bernoulli distribution of
demand occurancies; 3. croston, based on Croston, 1972 method with
SBA correction; 4. tsb, based on Teunter et al., 2011 method. 5.
auto - automatic selection of intermittency type based on information
criteria. The first letter can be used instead. 6. "sba" -
Syntetos-Boylan Approximation for Croston's method (bias correction)
discussed in Syntetos and Boylan, 2005.silent="none", then nothing is silent, everything is
printed out and drawn. silent="all" means that nothing is produced or
drawn (except for warnings). In case of silent="graph", no graph is
produced. If silent="legend", then legend of the graph is skipped.
And finally silent="output" means that nothing is printed out in the
console, but the graph is produced. silent also accepts TRUE
and FALSE. In this case silent=TRUE is equivalent to
silent="all", while silent=FALSE is equivalent to
silent="none". The parameter also accepts first letter of words ("n",
"a", "g", "l", "o").xreg should have number of observations equal either to
in-sample or to the whole series. If the number of observations in
xreg is equal to in-sample, then values for the holdout sample are
produced using es function."use" means that all of the data should be used, whilie
"select" means that a selection using ic should be done.
"combine" will be available at some point in future...xreg is NULL.TRUE, transition matrix for exogenous variables is
estimated, introducing non-linear interractions between parameters.
Prerequisite - non-NULL xreg.NULL, then estimated.
Prerequisite - non-NULL xreg.matrix(transition,nc,nc), where nc is number of components in
state vector. If NULL, then estimated. Prerequisite - non-NULL
xreg.FI=TRUE will
make the function also produce Fisher Information matrix, which then can be
used to calculated variances of parameters of the model. Maximum orders to
check can also be specified separately, however orders variable must
be set to NULL: ar.orders - Maximum order of AR term. Can be
vector, defining max orders of AR, SAR etc. i.orders - Maximum order
of I. Can be vector, defining max orders of I, SI etc. ma.orders -
Maximum order of MA term. Can be vector, defining max orders of MA, SMA etc.ets, es, ces,
sim.es, ges, ssarima
x <- rnorm(118,100,3)
# The best ARIMA for the data
ourModel <- auto.ssarima(x,orders=list(ar=c(2,1),i=c(1,1),ma=c(2,1)),lags=c(1,12),
h=18,holdout=TRUE,intervals="np")
# The other one using optimised states
## Not run: ------------------------------------
# auto.ssarima(x,orders=list(ar=c(3,2),i=c(2,1),ma=c(3,2)),lags=c(1,12),
# initial="o",h=18,holdout=TRUE)
## ---------------------------------------------
# And now combined ARIMA
## Not run: ------------------------------------
# auto.ssarima(x,orders=list(ar=c(3,2),i=c(2,1),ma=c(3,2)),lags=c(1,12),
# combine=TRUE,h=18,holdout=TRUE)
## ---------------------------------------------
summary(ourModel)
forecast(ourModel)
plot(forecast(ourModel))
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