R: Occurrence ETS, general model

oesg {smooth}

R Documentation

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, ivan@svetunkov.ru

Examples


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

[Package smooth version 4.0.2 Index]