Description Usage Arguments Details Value Author(s) References See Also Examples
Function returns the occurrence part of iETS model with the specified probability update and model types.
1 2 3 4 5 6 7 8 9  oes(y, model = "MNN", persistence = NULL, initial = "o",
initialSeason = NULL, phi = NULL, occurrence = c("fixed", "general",
"oddsratio", "inverseoddsratio", "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, ...)

y 
Either numeric vector or time series vector. 
model 
The type of ETS model used for the estimation. Normally this should
be 
persistence 
Persistence vector g, containing smoothing
parameters. If 
initial 
Can be either character or a vector of initial states. If it
is character, then it can be 
initialSeason 
The vector of the initial seasonal components. If 
phi 
The value of the dampening parameter. Used only for dampedtrend models. 
occurrence 
The type of model used in probability estimation. Can be

ic 
The information criteria to use in case of model selection. 
h 
The 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 
xreg 
The vector or the matrix of exogenous variables, explaining some parts of occurrence variable (probability). 
xregDo 
Variable defines what to do with the provided xreg:

initialX 
The vector of initial parameters for exogenous variables.
Ignored if 
updateX 
If 
transitionX 
The transition matrix F_x for exogenous variables. Can
be provided as a vector. Matrix will be formed using the default

persistenceX 
The persistence vector g_X, containing smoothing
parameters for exogenous variables. If 
... 
The parameters passed to the optimiser, such as 
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:
model
 the type of the estimated ETS model;
timeElapsed
 the time elapsed for the construction of the model;
fitted
 the fitted values for the probability;
fittedModel
 the fitted values of the underlying ETS model, where applicable
(only for occurrence=c("o","i","d"));
forecast
 the forecast of the probability for h
observations ahead;
forecastModel
 the forecast of the underlying ETS model, where applicable
(only for occurrence=c("o","i","d"));
lower
 the lower bound of the interval if interval!="none"
;
upper
 the upper bound of the interval if interval!="none"
;
lowerModel
 the lower bound of the interval of the underlying ETS model
if interval!="none"
;
upperModel
 the upper bound of the interval of the underlying ETS model
if interval!="none"
;
states
 the values of the state vector;
logLik
 the loglikelihood value of the model;
nParam
 the number of parameters in the model (the matrix is returned);
residuals
 the residuals of the model;
y
 actual values of occurrence (zeros and ones).
persistence
 the vector of smoothing parameters;
phi
 the value of the damped trend parameter;
initial
 initial values of the state vector;
initialSeason
 the matrix of initials seasonal states;
occurrence
 the type of the occurrence model;
updateX
 boolean, defining, if the states of exogenous variables were
estimated as well.
initialX
 initial values for parameters of exogenous variables.
persistenceX
 persistence vector g for exogenous variables.
transitionX
 transition matrix F for exogenous variables.
accuracy
 The error measures for the forecast (in case of holdout=TRUE
).
B
 the vector of all the estimated parameters (in case of "oddsratio",
"inverseoddsratio" and "direct" models).
Ivan Svetunkov, ivan@svetunkov.ru
Svetunkov Ivan and Boylan John E. (2017). Multiplicative StateSpace Models for Intermittent Time Series. Working Paper of Department of Management Science, Lancaster University, 2017:4 , 143.
Teunter R., Syntetos A., Babai Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214, 606615.
Croston, J. (1972) Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289303.
Syntetos, A., Boylan J. (2005) The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303314.
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