oesg: Occurrence ETS, general model

In smooth: Forecasting Using State Space Models

Occurrence ETS, general model

Description

Function returns the general occurrence model of the of iETS model.

Usage

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,
  bounds = c("usual", "admissible", "none"), silent = c("all", "graph",
  "legend", "output", "none"), xregA = NULL, xregB = NULL,
  initialXA = NULL, initialXB = NULL, regressorsA = c("use", "select"),
  regressorsB = c("use", "select"), ...)

Arguments

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 NULL, then estimated.
persistenceB	The persistence vector g, containing smoothing parameters used in the model B. If NULL, then estimated.
phiA	The value of the dampening parameter in the model A. Used only for damped-trend models.
phiB	The value of the dampening parameter in the model B. Used only for damped-trend models.
initialA	Either "o" - optimal or the vector of initials for the level and / or trend for the model A.
initialB	Either "o" - optimal or the vector of initials for the level and / or trend for the model B.
initialSeasonA	The vector of the initial seasonal components for the model A. If NULL, then it is estimated.
initialSeasonB	The vector of the initial seasonal components for the model B. If NULL, then it is estimated.
ic	Information criteria to use in case of model selection.
h	Forecast horizon.
holdout	If TRUE, holdout sample of size h is taken from the end of the data.
bounds	What type of bounds to use in the model estimation. The first letter can be used instead of the whole word.
silent	If 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").
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 xregA is NULL.
initialXB	The vector of initial parameters for exogenous variables in the model B. Ignored if xregB is NULL.
regressorsA	Variable defines what to do with the provided xregA: "use" means that all of the data should be used, while "select" means that a selection using ic should be done.
regressorsB	Similar to the regressorsA, but for the part B of the model.
...	The parameters passed to the optimiser, such as maxeval, xtol_rel, algorithm and print_level. The description of these is printed out by nloptr.print.options() function from the nloptr package. The default values in the oes function are maxeval=500, xtol_rel=1E-8, algorithm="NLOPT_LN_NELDERMEAD" and print_level=0.

Details

The function estimates probability of demand occurrence, based on the iETS_G state-space 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.

Value

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.

Author(s)

Ivan Svetunkov, [e-mail]

See Also

es, oes

Examples

y <- rpois(100,0.1)
oesg(y, modelA="MNN", modelB="ANN")

smooth documentation built on Oct. 1, 2024, 5:07 p.m.