Description Usage Arguments Details Value Author(s) See Also Examples
Function returns the general occurrence model of the of iETS model.
1 2 3 4 5 6 7 8 9 10 11  oesg(y, modelA = "MNN", modelB = "MNN", persistenceA = NULL,
persistenceB = NULL, phiA = NULL, phiB = NULL, initialA = "o",
initialB = "o", initialSeasonA = NULL, initialSeasonB = NULL,
ic = c("AICc", "AIC", "BIC", "BICc"), h = 10, holdout = FALSE,
interval = c("none", "parametric", "likelihood", "semiparametric",
"nonparametric"), level = 0.95, bounds = c("usual", "admissible",
"none"), silent = c("all", "graph", "legend", "output", "none"),
xregA = NULL, xregB = NULL, initialXA = NULL, initialXB = NULL,
xregDoA = c("use", "select"), xregDoB = c("use", "select"),
updateXA = FALSE, updateXB = FALSE, transitionXA = NULL,
transitionXB = NULL, persistenceXA = NULL, persistenceXB = NULL, ...)

y 
Either numeric vector or time series vector. 
modelA 
The type of the ETS for the model A. 
modelB 
The type of the ETS for the model B. 
persistenceA 
The persistence vector g, containing smoothing
parameters used in the model A. If 
persistenceB 
The persistence vector g, containing smoothing
parameters used in the model B. If 
phiA 
The value of the dampening parameter in the model A. Used only for dampedtrend models. 
phiB 
The value of the dampening parameter in the model B. Used only for dampedtrend models. 
initialA 
Either 
initialB 
Either 
initialSeasonA 
The vector of the initial seasonal components for the
model A. If 
initialSeasonB 
The vector of the initial seasonal components for the
model B. If 
ic 
Information criteria to use in case of model selection. 
h 
Forecast horizon. 
holdout 
If 
interval 
Type of interval to construct. This can be:
The parameter also accepts 
level 
Confidence level. Defines width of prediction interval. 
bounds 
What type of bounds to use in the model estimation. The first letter can be used instead of the whole word. 
silent 
If 
xregA 
The vector or the matrix of exogenous variables, explaining some parts of occurrence variable of the model A. 
xregB 
The vector or the matrix of exogenous variables, explaining some parts of occurrence variable of the model B. 
initialXA 
The vector of initial parameters for exogenous variables in the model
A. Ignored if 
initialXB 
The vector of initial parameters for exogenous variables in the model
B. Ignored if 
xregDoA 
Variable defines what to do with the provided 
xregDoB 
Similar to the 
updateXA 
If 
updateXB 
If 
transitionXA 
The transition matrix F_x for exogenous variables of the model A.
Can be provided as a vector. Matrix will be formed using the default

transitionXB 
The transition matrix F_x for exogenous variables of the model B.
Similar to the 
persistenceXA 
The persistence vector g_X, containing smoothing
parameters for the exogenous variables of the model A. If 
persistenceXB 
The persistence vector g_X, containing smoothing
parameters for the exogenous variables of the model B. If 
... 
The parameters passed to the optimiser, such as 
The function estimates probability of demand occurrence, based on the iETS_G statespace model. It involves the estimation and modelling of the two simultaneous state space equations. Thus two parts for the model type, persistence, initials etc.
For the details about the model and its implementation, see the respective
vignette: vignette("oes","smooth")
The model is based on:
o_t \sim Bernoulli(p_t)
p_t = \frac{a_t}{a_t+b_t}
,
where a_t and b_t are the parameters of the Beta distribution and are modelled using separate ETS models.
The object of class "occurrence" is returned. It contains following list of values:
modelA
 the model A of the class oes, that contains the output similar
to the one from the oes()
function;
modelB
 the model B of the class oes, that contains the output similar
to the one from the oes()
function.
B
 the vector of all the estimated parameters.
Ivan Svetunkov, ivan@svetunkov.ru
1 2 
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