R: Exponential smoothing forecasts

ses {forecast}

R Documentation

Exponential smoothing forecasts

Description

Returns forecasts and other information for exponential smoothing forecasts applied to x.

Usage

ses(x, h=10, level=c(80,95), fan=FALSE, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE, 
   level=c(80,95), fan=FALSE, ...)

Arguments

`x`	a numeric vector or time series
`h`	Number of periods for forecasting.
`damped`	If TRUE, use a damped trend.
`seasonal`	Type of seasonality in `hw` model. "additive" or "multiplicative"
`level`	Confidence level for prediction intervals.
`fan`	If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
`...`	Other arguments passed to `forecast.ets`.

Details

ses, holt and hw are simply convenient wrapper functions for forecast(ets(...)).

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

The generic accessor functions fitted.values and residuals extract useful features of the value returned by ets and associated functions.

An object of class "forecast" is a list containing at least the following elements:

`model`	A list containing information about the fitted model
`method`	The name of the forecasting method as a character string
`mean`	Point forecasts as a time series
`lower`	Lower limits for prediction intervals
`upper`	Upper limits for prediction intervals
`level`	The confidence values associated with the prediction intervals
`x`	The original time series (either `object` itself or the time series used to create the model stored as `object`).
`residuals`	Residuals from the fitted model. That is x minus fitted values.
`fitted`	Fitted values (one-step forecasts)

Author(s)

Rob J Hyndman

References

Hyndman, R.J., Koehler, A.B., Snyder, R.D., Grose, S. (2002) "A state space framework for automatic forecasting using exponential smoothing methods", International J. Forecasting, 18(3), 439–454.

Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible parameter space for exponential smoothing models". Annals of Statistical Mathematics, 60(2), 407–426.

Examples

fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)

[Package forecast version 3.24 Index]