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))Type of model fitted.
In-sample demand.
Out-of-sample demand.
Smoothing parameter.
Initialisation value.
Intermittent demand time series.
Forecast horizon.
Smoothing parameter. If w == NULL then parameter is optimised.
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 function used for optimisation: 1. "mar" - Mean Absolute Rate; 2. "msr" - Mean Squared Rate; 3. "mae" - Mean Absolute Error; 4. "mse" - Mean Squared Error.
If init.opt==TRUE then initial values are optimised.
If TRUE a plot of the forecast is provided.
This is meant to use only by the optimisation function. When opt.on is TRUE then no checks on inputs are performed.
A logical value indicating whether NA values should be remove using the method.
Nikolaos Kourentzes
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").
crost, tsb, crost.ma.