tsb: TSB (Teunter-Syntetos-Babai) method
In tsintermittent: Intermittent Time Series Forecasting

tsb	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.

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