| tsb {tsintermittent} | R Documentation |
TSB (Teunter-Syntetos-Babai) method
Description
TSB intermittent demand method with fixed or optimised parameters.
Usage
tsb(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 parameters. If w == NULL then parameters are optimised. Otherwise first parameter is for demand and second for demand probability. |
init |
Initial values for demand and intervals. This can be: 1. c(z,x) - Vector of two scalars, where first is initial demand and second is initial interval; 2. "naive" - Initial demand is first non-zero demand and initial demand probability is again the first one; 3. "mean" - Same as "naive", but initial demand probability is the mean of all in sample probabilities. |
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. |
Value
model |
Type of model fitted. |
frc.in |
In-sample demand rate. |
frc.out |
Out-of-sample demand rate. |
weights |
Smoothing parameters for demand and demand probability. |
initial |
Initialisation values for demand and demand probability smoothing. |
Author(s)
Nikolaos Kourentzes
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. doi: 10.1016/j.ijpe.2014.06.007.
See Also
Examples
tsb(ts.data1,outplot=TRUE)