# sexsm: Simple exponential smoothing In tsintermittent: Intermittent Time Series Forecasting

 sexsm R Documentation

## Simple exponential smoothing

### Description

Simple exponential smoothing with fixed or optimised parameters.

### Usage

```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.

### Value

 `model` Type of model fitted. `frc.in` In-sample demand. `frc.out` Out-of-sample demand. `alpha` Smoothing parameter. `initial` Initialisation value.

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