forecast (version 3.24)

ses: 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.

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:

  • modelA list containing information about the fitted model
  • methodThe name of the forecasting method as a character string
  • meanPoint forecasts as a time series
  • lowerLower limits for prediction intervals
  • upperUpper limits for prediction intervals
  • levelThe confidence values associated with the prediction intervals
  • xThe original time series (either object itself or the time series used to create the model stored as object).
  • residualsResiduals from the fitted model. That is x minus fitted values.
  • fittedFitted values (one-step forecasts)

Details

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

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.

See Also

ets, HoltWinters, rwf, arima.

Examples

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

Run the code above in your browser using DataCamp Workspace