expSmoot: Simple Exponential Smoothing Method In forecTheta: Forecasting Time Series by Theta Models

 expSmoot R Documentation

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

`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.