expSmoot: Simple Exponential Smoothing Method

In forecTheta: Forecasting Time Series by Theta Models

Simple Exponential Smoothing Method

Description

Estimation of Simple Exponential Smoothing Method

Usage

	expSmoot(y, h=5, ell0=NULL, alpha=NULL, lower = c(-1e+10, 0.1), 
	upper = c(1e+10, 0.99))

Arguments

y	Object of time series class.
h	Number of required forecasting periods.
ell0	The value of ell0^* parameter.
alpha	The value of alpha parameter.
lower	The lower limit of parametric space.
upper	The upper limit of parametric space.

Value

A list containing the elements:

$y	The original time series.
$par	The estimated values for (ell^*, alpha) parameters
$mean	The forecasting values
$fitted	A time series element with the fitted points.
$residuals	A time series element with the residual points.

Author(s)

Jose Augusto Fiorucci, Francisco Louzada and Bao Yiqi

See Also

forecTheta-package, stheta, dotm

Examples

y1 = 2+ 0.15*(1:20) + rnorm(20,2)
y2 = y1[20]+ 0.3*(1:30) + rnorm(30,2)
y =  as.ts(c(y1,y2))

expSmoot(y, h=10)

forecTheta documentation built on Nov. 12, 2022, 1:09 a.m.