oes: Occurrence ETS model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/oes.R


Function returns the occurrence part of iETS model with the specified probability update and model types.


oes(y, model = "MNN", persistence = NULL, initial = "o",
  initialSeason = NULL, phi = NULL, occurrence = c("fixed", "general",
  "odds-ratio", "inverse-odds-ratio", "direct", "auto", "none"),
  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"),
  xreg = NULL, xregDo = c("use", "select"), initialX = NULL,
  updateX = FALSE, transitionX = NULL, persistenceX = NULL, ...)



Either numeric vector or time series vector.


The type of ETS model used for the estimation. Normally this should be "MNN" or any other pure multiplicative or additive model. The model selection is available here (although it's not fast), so you can use, for example, "YYN" and "XXN" for selecting between the pure multiplicative and pure additive models respectively. Using mixed models is possible, but not recommended.


Persistence vector g, containing smoothing parameters. If NULL, then estimated.


Can be either character or a vector of initial states. If it is character, then it can be "optimal", meaning that the initial states are optimised, or "backcasting", meaning that the initials are produced using backcasting procedure.


The vector of the initial seasonal components. If NULL, then it is estimated.


The value of the dampening parameter. Used only for damped-trend models.


The type of model used in probability estimation. Can be "none" - none, "fixed" - constant probability, "odds-ratio" - the Odds-ratio model with b=1 in Beta distribution, "inverse-odds-ratio" - the model with a=1 in Beta distribution, "direct" - the TSB-like (Teunter et al., 2011) probability update mechanism a+b=1, "auto" - the automatically selected type of occurrence model, "general" - the general Beta model with two parameters. This will call oesg() function with two similar ETS models and the same provided parameters (initials and smoothing).


The information criteria to use in case of model selection.


The forecast horizon.


If TRUE, holdout sample of size h is taken from the end of the data.


Type of interval to construct. This can be:

  • "none", aka "n" - do not produce prediction interval.

  • "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. This type of interval relies on unbiased estimate of in-sample error variance, which divides the sume of squared errors by T-k rather than just T.

  • "likelihood", "l" - these are the same as "p", but relies on the biased estimate of variance from the likelihood (division by T, not by T-k).

  • "semiparametric", "sp" - interval based on covariance matrix of 1 to h steps ahead errors and assumption of normal / log-normal distribution (depending on error type).

  • "nonparametric", "np" - interval 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. The former means that parametric interval are constructed, while the latter is equivalent to none. If the forecasts of the models were combined, then the interval are combined quantile-wise (Lichtendahl et al., 2013).


Confidence level. Defines width of prediction interval.


What type of bounds to use in the model estimation. The first letter can be used instead of the whole word.


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").


The vector or the matrix of exogenous variables, explaining some parts of occurrence variable (probability).


Variable defines what to do with the provided xreg: "use" means that all of the data should be used, while "select" means that a selection using ic should be done. "combine" will be available at some point in future...


The vector of initial parameters for exogenous variables. Ignored if xreg is NULL.


If TRUE, transition matrix for exogenous variables is estimated, introducing non-linear interactions between parameters. Prerequisite - non-NULL xreg.


The transition matrix F_x for exogenous variables. Can be provided as a vector. Matrix will be formed using the default matrix(transition,nc,nc), where nc is number of components in state vector. If NULL, then estimated. Prerequisite - non-NULL xreg.


The persistence vector g_X, containing smoothing parameters for exogenous variables. If NULL, then estimated. Prerequisite - non-NULL xreg.


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_SBPLX" and print_level=0.


The function estimates probability of demand occurrence, using the selected ETS state space models.

For the details about the model and its implementation, see the respective vignette: vignette("oes","smooth")


The object of class "occurrence" is returned. It contains following list of values:


Ivan Svetunkov, ivan@svetunkov.ru


See Also

adam, oesg, es


y <- rpois(100,0.1)
oes(y, occurrence="auto")

oes(y, occurrence="f")

config-i1/smooth documentation built on June 16, 2021, 2:13 p.m.