# 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.

`crost`, `sexsm`, `crost.ma`.
```tsb(ts.data1,outplot=TRUE)