smooth (version 2.5.5)

iss: Intermittent State Space

Description

Function calculates the probability for intermittent state space model. This is needed in order to forecast intermittent demand using other functions.

Usage

iss(data, intermittent = c("none", "fixed", "interval", "probability", "sba",
  "logistic"), ic = c("AICc", "AIC", "BIC", "BICc"), h = 10,
  holdout = FALSE, model = NULL, persistence = NULL, initial = NULL,
  initialSeason = NULL, xreg = NULL)

Arguments

data

Either numeric vector or time series vector.

intermittent

Type of method used in probability estimation. Can be "none" - none, "fixed" - constant probability, "croston" - estimated using Croston, 1972 method and "TSB" - Teunter et al., 2011 method., "sba" - Syntetos-Boylan Approximation for Croston's method (bias correction) discussed in Syntetos and Boylan, 2005, "logistic" - probability based on logit model.

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.

model

Type of ETS model used for the estimation. Normally this should be either "ANN" or "MNN".

persistence

Persistence vector. If NULL, then it is estimated.

initial

Initial vector. If NULL, then it is estimated.

initialSeason

Initial vector of seasonal components. If NULL, then it is estimated.

xreg

Vector of matrix of exogenous variables, explaining some parts of occurrence variable (probability).

Value

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

• model - the type of the estimated ETS model;

• fitted - fitted values of the constructed model;

• forecast - forecast for h observations ahead;

• states - values of states (currently level only);

• variance - conditional variance of the forecast;

• logLik - likelihood value for the model

• nParam - number of parameters used in the model;

• residuals - residuals of the model;

• y - actual values of probabilities (zeros and ones).

• persistence - the vector of smoothing parameters;

• initial - initial values of the state vector;

• initialSeason - the matrix of initials seasonal states;

Details

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

References

• Svetunkov Ivan and Boylan John E. (2017). Multiplicative State-Space Models for Intermittent Time Series. Working Paper of Department of Management Science, Lancaster University, 2017:4 , 1-43.

• Teunter R., Syntetos A., Babai Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214, 606-615.

• Croston, J. (1972) Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289-303.

• Syntetos, A., Boylan J. (2005) The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303-314.

See Also

ets, forecast, es

Examples

# NOT RUN {
    y <- rpois(100,0.1)
    iss(y, intermittent="p")

    iss(y, intermittent="i", persistence=0.1)

# }