sexsm function

Simple exponential smoothing

Simple exponential smoothing

Simple exponential smoothing with fixed or optimised parameters.

sexsm(data,h=10,w=NULL,init=c("mean","naive"), cost=c("mar","msr","mae","mse"),init.opt=c(TRUE,FALSE), outplot=c(FALSE,TRUE),opt.on=c(FALSE,TRUE), na.rm=c(FALSE,TRUE))

Arguments

  • data: Intermittent demand time series.
  • h: Forecast horizon.
  • w: Smoothing parameter. If w == NULL then parameter is optimised.
  • init: Initial values for demand and intervals. This can be: 1. x - Numeric value for the initial level; 2. "naive" - Initial value is a naive forecast; 3. "mean" - Initial value is equal to the average of data.
  • cost: Cost function used for optimisation: 1. "mar" - Mean Absolute Rate; 2. "msr" - Mean Squared Rate; 3. "mae" - Mean Absolute Error; 4. "mse" - Mean Squared Error.
  • init.opt: If init.opt==TRUE then initial values are optimised.
  • outplot: If TRUE a plot of the forecast is provided.
  • opt.on: This is meant to use only by the optimisation function. When opt.on is TRUE then no checks on inputs are performed.
  • na.rm: A logical value indicating whether NA values should be remove using the method.

Returns

  • model: Type of model fitted.

  • frc.in: In-sample demand.

  • frc.out: Out-of-sample demand.

  • alpha: Smoothing parameter.

  • initial: Initialisation value.

References

Optimisation of the method described in: N. Kourentzes, 2014, On intermittent demand model optimisation and selection, International Journal of Production Economics, 156: 180-190. tools:::Rd_expr_doi("10.1016/j.ijpe.2014.06.007") .

https://kourentzes.com/forecasting/2014/06/11/on-intermittent-demand-model-optimisation-and-selection/

Author(s)

Nikolaos Kourentzes

See Also

crost, tsb, crost.ma.

Examples

sexsm(ts.data1,outplot=TRUE)