sexsm: Simple exponential smoothing

In tsintermittent: Intermittent Time Series Forecasting

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.

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

See Also

crost, tsb, crost.ma.

Examples

sexsm(ts.data1,outplot=TRUE)

tsintermittent documentation built on July 18, 2022, 9:06 a.m.